Microsoft Data Engineer Interview Preparation Guide - Mid Level
Microsoft's Data Engineer interview process for mid-level candidates (2-5 years experience) consists of an initial recruiter screening, followed by a 60-minute online technical assessment focused on SQL and coding fundamentals, and then four core virtual interview rounds evaluating SQL proficiency, data pipeline design, system architecture, and behavioral competencies. The entire process emphasizes your ability to design and optimize large-scale data systems, work effectively across teams, and demonstrate Microsoft's core values of learning and collaboration.
Interview Rounds
Recruiter Screening
What to Expect
This initial phone screening conducted by a Microsoft recruiter typically lasts 30-45 minutes. The recruiter validates your background, assesses your understanding of the role's scope and responsibilities, and gauges your genuine interest in Microsoft and the specific position. This conversation also provides an opportunity for you to ask questions about the team structure, project scope, and career development potential. Expect discussion of your data engineering experience, specific technical skills you've developed, and your motivation for the opportunity.
Tips & Advice
Be specific and concise when discussing your data engineering background—highlight 2-3 projects where you've meaningfully contributed to data systems, infrastructure, or pipeline improvements. Quantify impact where possible (e.g., reduced query latency by 60%, increased data processing capacity from 1M to 10M events daily). Demonstrate knowledge about the specific Microsoft products or teams you're interviewing for. Show enthusiasm for Azure and cloud-based data challenges. Ask informed questions that demonstrate you've researched the role—about the current data challenges the team faces, how the data engineering team collaborates with data scientists and analysts, or the scale of data systems you'd be working with. Be natural and conversational; this round also evaluates cultural alignment and communication style.
Focus Topics
Thoughtful Questions About Team and Project
Prepare 2-3 intelligent questions about the team's current challenges, how the data engineering team collaborates with data science and product organizations, whether there are ongoing initiatives you'd contribute to, or opportunities for technical growth and mentorship. Questions should reflect that you've researched the role and are thinking about how you'd contribute.
Practice Interview
Study Questions
Motivation for Microsoft and This Specific Role
Articulate why you're specifically interested in Microsoft—whether it's the scale of data challenges, Azure platform innovations, the company's impact on global technology, or specific products you admire. Connect your interests to specific aspects of the data engineering role (working with massive datasets, designing resilient systems, contributing to data governance, or enabling data-driven insights across Microsoft products).
Practice Interview
Study Questions
Understanding Role Responsibilities and Scope
Demonstrate that you understand this specific role: designing and implementing scalable data pipelines, building data warehouses and lakes, implementing ETL processes, ensuring data quality, optimizing storage and query performance, and monitoring pipeline health. Show awareness that the role involves collaborating with data scientists, analysts, and other engineering teams to ensure data accessibility and reliability.
Practice Interview
Study Questions
Technical Skills and Technology Stack
Discuss your proficiency with core technologies: SQL, Python or Java, Apache Spark, Hadoop, and cloud platforms. Emphasize any Azure experience (Azure Data Factory, Synapse, Data Lake Storage) since Microsoft prioritizes this. Mention familiarity with data warehouse design, ETL tools, and distributed systems concepts. Be honest about skill levels—confidence with emerging technologies is often valued more than false expertise.
Practice Interview
Study Questions
Relevant Data Engineering Experience and Project Impact
Articulate your 2-5 years of data engineering experience with concrete examples. Discuss specific projects where you built data pipelines, optimized queries, designed data architecture, or resolved data challenges. Describe the data scale (volume and velocity), technologies used (SQL, Python, Spark, Hadoop), team structure, and measurable outcomes of your work. Connect your past experience directly to the responsibilities outlined in the job description.
Practice Interview
Study Questions
Online Technical Assessment
What to Expect
Typically 1-2 weeks after your recruiter screening, you'll receive a link to a 60-minute timed technical assessment. This automated test focuses on SQL query writing and coding problem-solving relevant to data engineering at scale. You'll have a specified window (usually 7 days) to complete the assessment. The test comprises 1-2 SQL problems and 1-2 algorithmic coding problems designed to evaluate your ability to manipulate data, write optimized queries, think through edge cases, and solve problems systematically. This assessment screens for baseline technical competency and determines whether you advance to the core interview rounds.
Tips & Advice
Find a quiet, distraction-free environment with reliable internet connection—treat this like a formal assessment. Read each problem carefully before beginning; understanding requirements precisely is critical. For SQL problems, start with a basic correct solution, then consider optimizations and edge cases. For coding problems, spend 2-3 minutes thinking through your approach before writing code; clear thinking upfront prevents error-prone coding. Use meaningful variable names and add brief explanatory comments. If you're unsure about a solution after 8-10 minutes, document your approach in pseudocode or comments and move to the next problem—you can return with remaining time. Test your logic mentally with simple examples. Optimize for correctness first, then efficiency. Practice similar problems on LeetCode (medium-level SQL and algorithmic problems) 2-3 weeks before your assessment.
Focus Topics
Computational Complexity and Efficiency Awareness
Understand Big O notation basics and analyze the time and space complexity of your solutions. Choose algorithms and data structures appropriate for the problem scale. For mid-level, you don't need encyclopedic algorithm knowledge, but should recognize inefficient approaches (e.g., nested loops for large datasets) and be able to justify your complexity choices.
Practice Interview
Study Questions
Data Transformation and Manipulation Logic
Problems often involve transforming data structures, applying business logic to datasets, handling invalid or missing data, and performing aggregations or groupings. Demonstrate the ability to think through data flow logically and write code that correctly handles real-world scenarios—duplicates, NULL values, empty datasets, schema inconsistencies.
Practice Interview
Study Questions
SQL Query Writing and Optimization
Write efficient SQL queries involving complex joins (INNER, LEFT, RIGHT, FULL OUTER), CTEs (Common Table Expressions), subqueries, window functions (ROW_NUMBER, RANK, LAG, LEAD), aggregations with GROUP BY and HAVING, and proper NULL handling. Understand how to analyze query performance and identify optimization opportunities—rewriting inefficient joins, using appropriate indexes, restructuring subqueries. Handle business logic requirements correctly: filtering, sorting, ranking, and deriving new fields. Test solutions mentally for edge cases: empty result sets, duplicate values, NULL values.
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Study Questions
Algorithmic Problem-Solving in Python or Java
Solve medium-level algorithmic problems using your preferred language. Common topics include data structure manipulation (arrays, lists, dictionaries/maps, sets, strings), searching and sorting algorithms, two-pointer techniques, and recursion basics. Write clean code with appropriate variable naming. Understand time and space complexity implications of your approaches. Be comfortable with standard library functions but also able to implement solutions from scratch when required. Demonstrate edge case awareness.
Practice Interview
Study Questions
SQL Coding Interview
What to Expect
This 60-minute virtual interview with a Microsoft engineer focuses on SQL expertise. You'll be presented with 1-2 SQL problems of increasing complexity, typically involving writing queries to answer business questions, optimizing slow queries, or designing efficient queries for large datasets. The interviewer observes your problem-solving approach, code quality, and communication as you work through problems. They may ask follow-up questions, request query optimization or alternative approaches, or discuss how your solution scales to billions of rows. This round directly evaluates your SQL mastery—a core competency for data engineers.
Tips & Advice
Start by clarifying the problem: ask about table structure, data volume, expected output format, performance requirements, and any constraints. Explain your understanding back to the interviewer. Write your query incrementally—start with a basic correct solution, then optimize. Be explicit about assumptions and edge cases. If you encounter syntax uncertainty, explain your intention clearly ('I'd use a window function here to rank by...'). When asked to optimize, discuss multiple approaches and their trade-offs before committing to a final query. Be prepared to explain how your solution would perform with 1 billion rows and discuss scaling strategies. Articulate your thinking as you code—interviewers value understanding your reasoning process. If stuck, think out loud rather than sitting silently; interviewers often provide hints if they understand where you're stuck. Practice writing queries on a whiteboard or shared document to simulate the interview setting.
Focus Topics
Database Schema and Relational Database Concepts
Understand foundational database design: primary keys, foreign keys, normalization principles, and the difference between normalized and denormalized schemas. Recognize OLTP (Online Transaction Processing) versus OLAP (Online Analytical Processing) systems and how query patterns differ. Understand data warehouse schema design concepts like star schema and snowflake schema. These concepts inform how you write efficient queries against various database designs.
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Study Questions
Communication and Collaborative Problem-Solving
Clearly articulate your understanding of the problem before writing code. Explain your approach and the reasoning behind your choices. Discuss scale considerations—your query performance, indexing strategy, and optimization approach should account for varying data sizes. Be open to interviewer feedback and suggestions. Explain your thinking as you write rather than working silently. Ask clarifying questions when requirements are ambiguous. This demonstrates maturity and collaborative mindset.
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Study Questions
Data Quality Considerations and Edge Case Handling
Write queries that correctly handle real-world data challenges: NULL values (understanding NULL semantics in comparisons and aggregations), duplicate records, invalid or out-of-range values, and schema inconsistencies. When solving business problems, consider edge cases: what if the result set is empty? What if there are multiple records matching a condition when only one is expected? Implement appropriate error handling or data validation in your queries.
Practice Interview
Study Questions
Complex Multi-Step SQL Query Construction
Write SQL queries that solve multi-layered business problems requiring joins across multiple tables, nested aggregations, filtering with HAVING clauses, window functions for ranking or calculating running totals, CTEs for code clarity, and self-joins for hierarchical data. Understand different join types deeply and know when to use each. Handle GROUP BY with aggregations correctly, distinguishing between row-level and aggregated filtering.
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Study Questions
Query Performance Optimization and Execution Plans
Identify performance bottlenecks in slow queries. Understand how indexing improves query performance and when indexes can actually hurt performance (due to scan costs). Optimize queries through techniques like reordering join conditions, using CTEs to improve readability and potentially performance, avoiding expensive operations (e.g., full table scans, Cartesian products), and restructuring subqueries. Discuss query execution plans conceptually. Justify optimization choices with reasoning about how they reduce computational cost.
Practice Interview
Study Questions
Data Pipeline and ETL Design Interview
What to Expect
In this 60-minute interview, you'll be asked to design end-to-end data pipelines or ETL systems addressing real scenarios. For example: 'Design a data pipeline that processes telemetry data from a million devices,' 'Design an ETL system that integrates data from multiple sources with different schemas,' or 'Design a scalable data lake architecture for a social media platform.' You're expected to discuss data ingestion strategies, storage architecture, transformation logic, quality assurance, monitoring, and fault tolerance. The interviewer emphasizes Azure-native solutions (Azure Data Factory, Synapse, Data Lake Storage) while also evaluating your understanding of broader big data technologies. This round assesses your ability to design systems that balance scalability, reliability, cost, and operational simplicity.
Tips & Advice
Begin by gathering requirements and constraints through targeted questions: How much data? What's the velocity? What are latency requirements? Do we need real-time or batch processing? What consistency guarantees are required? What's the budget/cost sensitivity? Once you understand requirements, propose a high-level architecture with major components: ingestion, storage, processing, serving, monitoring. Use diagrams to communicate your design. Start with Azure services for cloud components, but also discuss open-source alternatives and when you'd choose each. For each architectural component, discuss technology choices and trade-offs explicitly. Address operational concerns early: how do you monitor pipeline health? How do you debug issues? How do you handle schema evolution or late-arriving data? For mid-level, you're not expected to have designed multi-billion user systems, but should demonstrate solid grasp of scalability principles and be able to reason through design trade-offs. Engage in back-and-forth discussion—respond to interviewer questions and adjust your design based on feedback. Acknowledge uncertainty ('I'd need more information about X') rather than guessing.
Focus Topics
Batch vs. Stream Processing Trade-offs
Understand when batch processing is appropriate (historical data, nightly jobs, cost optimization) versus stream processing (real-time dashboards, immediate alerts, event-driven systems). Discuss latency, complexity, operational overhead, and cost implications of each. Know representative batch technologies (scheduled jobs, Spark batch processing, Hive) and streaming technologies (Kafka, Azure Event Hubs, Spark Structured Streaming). For a given scenario, justify your processing choice based on requirements.
Practice Interview
Study Questions
Data Storage Optimization and Cost Management
Design storage strategies that optimize for your access patterns. Discuss partitioning schemes (by date, by geography, by key) to enable efficient querying and parallel processing. Choose appropriate file formats (Parquet, ORC, Delta) and understand their trade-offs. Plan for data lifecycle management: raw data in cheap cold storage transitioning to hot storage as needed. Discuss how to handle data accumulation over time and prevent storage costs from escalating. Consider cost-performance trade-offs in your design.
Practice Interview
Study Questions
Data Quality, Validation, and Data Governance
Design mechanisms to detect and prevent data quality issues throughout the pipeline: schema validation at ingestion, range and format checks, completeness checks (no unexpected missing values), duplicate detection, and handling late-arriving or out-of-order data. Implement data lineage tracking to understand data provenance. Plan monitoring to surface data quality metrics to stakeholders. Design how you'd identify and investigate quality issues. Discuss data governance controls: data access policies, PII handling, data retention policies, and audit logging.
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Study Questions
Scalability and Fault-Tolerant Architecture Design
Design systems that reliably process data at massive scale (billions of events daily) and gracefully handle failures. Discuss how you'd partition data for parallel processing, distribute computation across multiple nodes, and recover from node failures without losing data. Understand idempotent operations and exactly-once processing semantics. For batch pipelines, discuss retry strategies and recovery mechanisms. For streaming pipelines, discuss windowing, state management, and handling late-arriving data. Explain how your design decisions impact cost, latency, and operational complexity. Address CAP theorem trade-offs in your design.
Practice Interview
Study Questions
Azure Data Services Integration and Selection
Demonstrate familiarity with Microsoft's data platform services: Azure Data Factory for orchestration and data movement, Azure Data Lake Storage for scalable data storage, Azure Synapse Analytics for data warehousing and analytics, and Azure Databricks for Spark-based transformations. Discuss when to use each service and how they integrate. Know Azure Data Factory concepts: Copy Activity for data movement, Data Flows for transformations, linked services, integration runtimes. Understand Data Lake Storage tiers (Hot, Cool, Archive) and when to use each. Show awareness of alternatives (on-premises data movements, hybrid scenarios) but emphasize Azure-native solutions.
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Study Questions
End-to-End ETL Pipeline Architecture
Design complete Extract-Transform-Load workflows from data source ingestion through final serving layer. In the Extract phase, discuss data collection mechanisms (batch import, streaming ingestion, API polling), handling diverse data formats and sources, and ensuring no data loss. In the Transform phase, describe data cleaning, validation, business logic application, aggregations, and how you'd implement these using tools like Spark or Azure Data Factory Data Flows. In the Load phase, discuss target systems (data warehouse, data lake, reporting tools) and loading strategies (full loads, incremental updates, upserts). Address data validation at each stage.
Practice Interview
Study Questions
System Design Interview
What to Expect
This 60-minute interview evaluates your ability to design large-scale data systems from first principles. You might be asked: 'Design a data processing system for a social media platform generating billions of events daily,' 'Design a real-time analytics platform for an e-commerce company,' or 'Design a data lake architecture for a financial services company.' Unlike the pipeline design round which focuses on ETL workflows, this round emphasizes overall system architecture, trade-offs between competing concerns (consistency vs. availability, cost vs. performance), and design decisions that scale to massive scope. You'll discuss data ingestion architecture, storage layer design, processing frameworks, consistency models, operational observability, and security/governance. The interviewer looks for systematic thinking, awareness of distributed systems challenges, and ability to evolve designs based on requirements.
Tips & Advice
Start by understanding requirements deeply through targeted questions: What's the data volume (events/day), velocity (events/second), and expected growth? What are latency requirements (batch, near real-time, real-time)? What consistency guarantees are needed? What's the budget? Who are the end users of this system? These answers shape your entire design. Propose a high-level architecture breaking the system into logical components: data sources, ingestion layer, storage layer, processing layer, serving layer, and monitoring. Use diagrams to visualize information flow. For each component, discuss technology choices and justify them. Address scaling explicitly—how does your system handle 2x, 10x, 100x growth? Discuss failure scenarios: What happens when a service goes down? When data becomes corrupted? When demand spikes unexpectedly? For mid-level, you're not expected to have built Facebook-scale systems, but should demonstrate solid understanding of distributed systems principles and ability to think through trade-offs. Discuss trade-offs explicitly ('If we choose strong consistency here, we sacrifice availability, so we'd need...'), don't just list technologies. Engage in dialogue—respond to interviewer challenges and be willing to reconsider your design. Say 'I'd need to know more' rather than guessing.
Focus Topics
Security, Governance, and Compliance in Large-Scale Systems
Address security at multiple layers: authentication/authorization (who can access what data), encryption in transit and at rest, network security, and access control. Discuss role-based access control (RBAC) and least-privilege principles. Implement audit logging for sensitive data access. Design data lineage tracking and data governance tools. Address compliance requirements (GDPR, HIPAA, data residency) where applicable. Plan PII (Personally Identifiable Information) handling strategies. Make security and governance architectural decisions, not bolt-on afterthoughts.
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Study Questions
Monitoring, Observability, and Operational Resilience
Design operational visibility into your data system: metrics for pipeline health (throughput, latency, error rates, data arrival delays), data quality metrics (completeness, accuracy), and resource utilization (CPU, memory, storage). Plan alerting strategies for anomalies. Design logging and tracing to support debugging production issues. Discuss runbooks for common failure scenarios. Design graceful degradation—when subsystems fail, how does the system respond? Discuss chaos engineering and resilience testing approaches. Make monitoring and operational excellence equal priorities to functionality.
Practice Interview
Study Questions
Real-Time vs. Batch Processing Architecture
Understand when to implement batch processing (nightly jobs, weekly aggregations, historical analytics) versus stream processing (real-time dashboards, immediate alerts, live dashboards). Discuss the latency, complexity, cost, and operational implications of each. Consider hybrid architectures like the Lambda architecture (batch + streaming) or Kappa architecture (streaming only with reprocessing). Make conscious architectural choices based on requirements, not technology preference. Know that real-time systems are more operationally complex but necessary for some use cases.
Practice Interview
Study Questions
Data Lake and Data Warehouse Architecture
Design the storage architecture of your data system, typically comprising both data lakes (for raw, unprocessed data) and data warehouses (for structured, processed data). Choose storage technologies (Azure Data Lake Storage for lakes, Synapse or traditional data warehouses for warehouses). Discuss partitioning strategies (by date, geography, key) that enable efficient querying and parallel processing. Choose serialization formats (Parquet, ORC, Delta) and justify decisions. Address schema evolution over time—how do you handle schema changes without breaking existing pipelines? Plan data lifecycle management: raw data aging to cold storage, derived data, temporary intermediate data cleanup.
Practice Interview
Study Questions
High-Throughput Data Ingestion at Scale
Design ingestion systems that reliably collect data from millions of sources generating billions of events daily. Discuss event collection from diverse producers (mobile apps, servers, sensors), handling traffic spikes and ensuring zero data loss. Consider technologies like Azure Event Hubs for high-throughput ingestion, message queues for decoupling producers from processors, and retry/dead-letter queue patterns for reliability. Address partitioning strategies to parallelize ingestion. Discuss data ordering guarantees (needed in some scenarios, not others) and exactly-once semantics. Handle backpressure—what happens when consuming systems can't keep up?
Practice Interview
Study Questions
Distributed Systems Principles and Trade-offs
Understand core distributed systems concepts applicable to data systems: fault tolerance, consistency models (strong vs. eventual consistency), replication strategies, partitioning, and consensus algorithms. Know how the CAP theorem applies—you can have at most two of Consistency, Availability, and Partition tolerance. Discuss when you'd prioritize each. Understand the spectrum between strongly consistent systems (higher latency, lower availability) and eventually consistent systems (lower latency, higher availability). Discuss these trade-offs explicitly in your design—why did you choose this consistency model for this component?
Practice Interview
Study Questions
Behavioral Interview
What to Expect
This 60-minute interview assesses how you work with teams, approach challenges, handle ambiguity and conflict, and align with Microsoft's core values. The interviewer asks behavioral questions ('Tell me about a time when...') and situational questions ('How would you handle...?') to understand your collaboration style, communication approach, problem-solving methodology, growth mindset, and ownership mentality. For mid-level candidates, interviewers focus on evidence that you can own medium-sized projects independently, mentor junior colleagues effectively, collaborate across team boundaries, learn from setbacks, and contribute to team decisions. They look for maturity in handling ambiguity and conflict, resilience in face of challenges, and genuine passion for continuous improvement.
Tips & Advice
Prepare 5-7 specific stories from your experience using the STAR method (Situation, Task, Action, Result). Stories should showcase different competencies: technical problem-solving, cross-functional collaboration, handling failure and learning, taking initiative, mentoring junior colleagues, and managing ambiguity. Use stories flexibly to answer various questions rather than rigidly reciting them. Emphasize your personal contribution—use 'I' more than 'we,' and be specific about your actions and decisions. Quantify outcomes when possible: not 'improved performance' but 'reduced pipeline execution time from 45 minutes to 12 minutes, saving $2K monthly in compute costs.' For failure stories, show genuine learning—discuss what you'd do differently and how that experience changed your approach. Be authentic rather than polished. Answer the question asked; if unsure what's being asked, clarify. Demonstrate genuine curiosity about Microsoft's mission and data challenges. At the end, ask meaningful questions about team dynamics, technical challenges, growth opportunities, and how success is measured in the role.
Focus Topics
Handling Ambiguity, Setbacks, and Interpersonal Conflict
Prepare examples of situations where requirements were unclear, projects faced unexpected obstacles, or you disagreed with teammates. Show how you clarified ambiguous requirements, communicated concerns respectfully, adapted your approach, and worked through disagreements productively. Demonstrate maturity: not blaming others but focusing on solutions. Show resilience in face of setbacks—discussing what you learned rather than dwelling on failure. This reflects real-world work where ambiguity and conflict are normal.
Practice Interview
Study Questions
Technical Problem-Solving and Systematic Debugging
Describe a complex technical problem you investigated and resolved. Walk through your systematic approach: identifying symptoms, forming hypotheses about root causes, testing assumptions, gathering evidence, iterating on solutions. Highlight tools and techniques you used. Show how you broke down a seemingly intractable problem into manageable pieces. Discuss what you learned and how that experience changed your problem-solving approach going forward. This demonstrates depth of technical thinking beyond surface-level coding.
Practice Interview
Study Questions
Learning Agility and Continuous Growth
Describe situations where you learned new technologies, frameworks, or domains quickly to complete projects or fill gaps. Show your learning approach: reading documentation, experimenting, seeking mentorship, building proof-of-concepts. Discuss how you stay current with evolving technologies and industry trends. Show examples of skills developed since joining your current role. For mid-level, demonstrate that you're actively developing into a senior engineer through intentional learning.
Practice Interview
Study Questions
Data Quality Issue Resolution and Accountability
Provide an example of discovering and resolving a data quality issue (incorrect data, missing records, schema inconsistency) that impacted downstream users. Discuss how you identified the root cause, communicated the impact to stakeholders, implemented a fix, and took steps to prevent recurrence. Show accountability and proactive thinking. Discuss trade-offs between speed of response and careful diagnosis. This is directly relevant since the job includes 'troubleshooting data quality issues.'
Practice Interview
Study Questions
Ownership and End-to-End Project Delivery
Describe a medium-sized project you owned from initial design through production deployment. Discuss your responsibilities, technical decisions you made, challenges encountered, how you managed trade-offs, and how you drove it to completion. Show that you understood impact beyond just coding—you considered user needs, monitored performance, responded to issues, and iterated on improvements. Demonstrate ownership mentality: not just assigned tasks but genuinely caring about outcomes and quality. For mid-level, project scope should be meaningful but not company-transforming.
Practice Interview
Study Questions
Cross-Functional Collaboration and Stakeholder Communication
Prepare examples demonstrating effective collaboration with diverse teams: data scientists who use your pipelines, analysts who need specific data structures, product managers driving requirements, and other engineers supporting your work. Discuss how you understood different stakeholders' needs, adapted communication to different audiences, negotiated competing priorities, and delivered solutions that satisfied multiple parties. Show how you actively incorporated feedback and adjusted based on stakeholder input. For mid-level, demonstrate growing influence—you don't have formal authority but drive decisions through technical credibility and clear communication.
Practice Interview
Study Questions
Frequently Asked Data Engineer Interview Questions
Sample Answer
Sample Answer
-- merge staging into target using natural key to make sink idempotent
MERGE INTO target_table t
USING staging_table s
ON t.natural_key = s.natural_key
WHEN MATCHED THEN
UPDATE SET t.col1 = s.col1, t.updated_at = s.updated_at
WHEN NOT MATCHED THEN
INSERT (natural_key, col1, updated_at) VALUES (s.natural_key, s.col1, s.updated_at);-- dedupe staging first, then insert ignoring existing keys
WITH dedup AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY natural_key ORDER BY event_time DESC) rn
FROM raw_events
)
INSERT INTO target_table (natural_key, col1, event_time)
SELECT natural_key, col1, event_time
FROM dedup WHERE rn = 1
ON CONFLICT (natural_key) DO NOTHING;Sample Answer
Sample Answer
class Node:
def __init__(self, k, v):
self.k, self.v = k, v
self.prev = self.next = None
class LRUCache:
def __init__(self, capacity: int):
self.cap = capacity
self.map = {} # key -> Node
# dummy head/tail to simplify edge ops
self.head, self.tail = Node(None, None), Node(None, None)
self.head.next, self.tail.prev = self.tail, self.head
def _remove(self, node):
# unlink node
p, n = node.prev, node.next
p.next, n.prev = n, p
def _add_to_head(self, node):
node.next = self.head.next
node.prev = self.head
self.head.next.prev = node
self.head.next = node
def get(self, key):
node = self.map.get(key)
if not node:
return -1
self._remove(node)
self._add_to_head(node)
return node.v
def put(self, key, value):
node = self.map.get(key)
if node:
node.v = value
self._remove(node)
self._add_to_head(node)
return
if len(self.map) >= self.cap:
# evict least recently used (tail.prev)
lru = self.tail.prev
self._remove(lru)
del self.map[lru.k]
newn = Node(key, value)
self.map[key] = newn
self._add_to_head(newn)Sample Answer
Sample Answer
Sample Answer
Sample Answer
SELECT order_date,
percentile_cont(0.5) WITHIN GROUP (ORDER BY amount) AS median_amount
FROM orders
GROUP BY order_date
ORDER BY order_date;SELECT DISTINCT order_date,
percentile_cont(0.5) WITHIN GROUP (ORDER BY amount) OVER (PARTITION BY order_date) AS median_amount
FROM orders;SELECT order_date,
approx_percentile(amount, 0.5) AS median_approx
FROM orders
GROUP BY order_date;SELECT order_date,
APPROX_QUANTILES(amount, 100)[OFFSET(50)] AS median_approx
FROM dataset.orders
GROUP BY order_date;Sample Answer
Sample Answer
import hashlib
import struct
from math import ceil, log, exp
class SimpleBloom:
def __init__(self, expected_n, fp_rate):
# compute size m (bits) and k (hashes) for desired fp_rate
self.n = expected_n
self.m = max(8, int(ceil((-expected_n * log(fp_rate)) / (log(2)**2)))) # bits
self.k = max(1, int(round((self.m / expected_n) * log(2))))
self.bytes = bytearray((self.m + 7) // 8)
self.count = 0
def _hashes(self, item):
b = item if isinstance(item, bytes) else str(item).encode('utf-8')
h1 = int.from_bytes(hashlib.sha256(b).digest()[:8], 'big')
h2 = int.from_bytes(hashlib.md5(b).digest()[:8], 'big')
for i in range(self.k):
yield (h1 + i * h2) % self.m
def add(self, item):
for pos in self._hashes(item):
self.bytes[pos // 8] |= 1 << (pos % 8)
self.count += 1
def maybe_contains(self, item):
return all((self.bytes[pos // 8] >> (pos % 8)) & 1 for pos in self._hashes(item))
def serialize(self, path):
# write header: m,k,count (3 x 8 bytes) + raw bytes
with open(path, 'wb') as f:
f.write(struct.pack('>QQQ', self.m, self.k, self.count))
f.write(self.bytes)
@classmethod
def deserialize(cls, path):
with open(path,'rb') as f:
m,k,count = struct.unpack('>QQQ', f.read(24))
b = bytearray(f.read())
obj = cls(1, 0.1) # temporary init
obj.m, obj.k, obj.bytes, obj.count = m, k, b, count
return objRecommended Additional Resources
- Cracking the Coding Interview by Gayle Laakmann McDowell—comprehensive resource for algorithm fundamentals and system design thinking
- Designing Data-Intensive Applications by Martin Kleppmann—authoritative reference on distributed systems, storage, and processing concepts critical to data engineering
- LeetCode SQL and Medium-level algorithmic problems—platform for practicing SQL queries and coding problems similar to Microsoft assessments
- Databricks Academy—comprehensive Apache Spark and data engineering fundamentals training
- Official Microsoft Learn Azure Data Engineering Learning Path—tutorials covering Azure Data Factory, Synapse Analytics, and Data Lake Storage
- InterviewQuery—curated data engineering and SQL interview questions with detailed explanations
- Levels.fyi and Blind—platforms where current and former Microsoft engineers share interview experiences and insights
- Microsoft Official Documentation—Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake Storage reference materials
- System Design Interview by Alex Xu and Grokking the System Design Interview—practical system design patterns applicable to data systems
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