Amazon Staff Data Engineer Interview Preparation Guide
Amazon's Staff Data Engineer interview process is a rigorous, multi-stage evaluation designed to assess technical expertise, system design capabilities, leadership qualities, and cultural alignment. The process spans approximately 6-8 weeks from initial application to offer. It includes an initial recruiter screening, a technical phone screen focusing on SQL and coding, followed by a comprehensive onsite evaluation consisting of multiple technical interviews emphasizing data architecture and system design, a bar raiser round assessing elevated standards and leadership principles, and a final interview with the hiring manager focused on team fit and strategic impact. For Staff-level candidates, the evaluation emphasizes architectural thinking, mentorship capability, and strategic impact alongside technical mastery.
Interview Rounds
Recruiter Screening
What to Expect
The initial recruiter screening typically lasts 20-30 minutes and serves as a mutual fit assessment. The recruiter will review your background, experience in data engineering, and motivation for the role at Amazon. They'll discuss your experience with large-scale data systems, previous projects, and your understanding of Amazon's business and culture. This round establishes baseline qualifications and determines if you're a suitable candidate to move forward. The recruiter will also discuss compensation expectations, visa sponsorship if needed, and logistics for subsequent rounds.
Tips & Advice
Research Amazon's business model and how data engineering underpins operations at scale. Prepare a clear 2-3 minute overview of your career trajectory, emphasizing leadership growth and increasingly complex data systems you've architected and owned. Highlight specific projects where you made significant architectural decisions or successfully mentored and developed junior engineers. Practice the Tell me about yourself introduction, focusing on scale of systems, business impact achieved, and leadership responsibilities. Ask thoughtful questions about team structure, current priorities, and how data engineering contributes to Amazon's mission. Be genuine about your interest in Amazon specifically, not just any large tech company. Demonstrate that you've thought about why Staff-level role at Amazon is the right next step in your career.
Focus Topics
Motivation for Amazon and Career Stage Fit
Articulate why Amazon specifically appeals to you at this stage of your career. Discuss what aspects of Amazon's technology challenges, scale, culture, or mission resonate with you. Explain how this Staff-level role aligns with your career goals and what you want to accomplish in the next phase.
Practice Interview
Study Questions
Understanding of Amazon's Business and Data Infrastructure
Research and discuss how Amazon uses data across its various business units (retail, AWS, logistics, advertising, Prime). Understand the scale of data Amazon processes globally and how data engineering supports Amazon's core values of customer obsession and operational excellence. Show specific knowledge about Amazon's infrastructure challenges.
Practice Interview
Study Questions
Professional Background and Career Trajectory at Staff Level
Articulate your 12+ years of career progression in data engineering, highlighting the evolution of responsibility from individual contributor through senior roles to staff-level leadership. Discuss the scale of systems you've owned, from data volume and processing complexity to team leadership scope and organizational influence. Emphasize how you've grown from purely technical roles into positions where you set direction and influence strategy.
Practice Interview
Study Questions
Large-Scale Data Engineering Systems and Architecture Ownership
Prepare detailed examples of large-scale data engineering projects you've led, including architecture decisions, technology choices, team size managed, business impact achieved, and challenges overcome. Focus on projects demonstrating petabyte or exabyte-scale data, reliability under extreme conditions, and systems that enable critical business operations.
Practice Interview
Study Questions
Leadership, Mentorship, and Organizational Impact
Discuss your experience leading engineers, building and scaling teams, and developing talent into senior roles. Provide examples of mentoring relationships, how you've grown junior and mid-level engineers into senior positions, and how you've influenced technical direction across your organization. Discuss how you've shaped engineering culture and practices.
Practice Interview
Study Questions
Technical Phone Screen
What to Expect
The technical phone screen, typically 45-60 minutes, is conducted by a data engineer and focuses on assessing SQL proficiency, data modeling understanding, and coding problem-solving ability. The interviewer will present SQL query problems ranging from intermediate to complex, and may include data modeling design decisions. A coding problem in Python or Java may also be included to assess algorithmic thinking and optimization ability. For Staff-level candidates, expect questions that explore optimization deeply, trade-offs in system design, and architectural thinking even within these fundamentals.
Tips & Advice
Treat this as a real-time coding interview with a whiteboard or collaborative coding environment. Verbalize your thought process clearly before jumping into code or queries. For SQL questions, discuss query optimization approaches, explain index usage, join strategies, query execution plans, and how your solution would scale to larger datasets. For data modeling, sketch the schema and explain your decisions about normalization, denormalization, indexing trade-offs, and how it supports analytical queries at scale. For coding problems, break down the problem methodically, discuss edge cases, consider time and space complexity trade-offs, and optimize your solution. For Staff-level candidates, emphasize understanding fundamental trade-offs and scalability implications of your choices. Be prepared to discuss how your solution would perform at 100x current scale. Practice on LeetCode (SQL and medium-to-hard problems) to build speed and confidence. Time yourself to ensure you can complete problems confidently within 45-60 minutes.
Focus Topics
ETL Concepts and Data Flow Understanding
Understand Extract-Transform-Load processes at a conceptual level. Discuss how data moves from source systems to data warehouses, transformation logic, error handling, idempotency, and recovery mechanisms. Understand batch vs. real-time processing and when each is appropriate. Discuss late-arriving facts and data quality.
Practice Interview
Study Questions
Python Coding and Algorithmic Problem Solving
Demonstrate proficiency in Python (or Java if preferred) to solve algorithmic problems efficiently. Focus on data manipulation, string processing, array and collection operations, and algorithm efficiency. Understand Big O notation and be able to optimize solutions. Be comfortable with sorting, searching, and basic dynamic programming. For Staff-level, emphasize thinking about scalability and edge cases.
Practice Interview
Study Questions
Data Modeling Principles and Schema Design
Understand both normalized (3NF, BCNF) and denormalized schema designs. Know when to use star schema, snowflake schema, or other dimensional modeling approaches. Discuss trade-offs between normalization (fewer redundancies, better for OLTP) and denormalization (better query performance, suitable for OLAP). Practice designing schemas for complex business scenarios. For Staff-level, discuss implications of schema design choices for performance, maintenance, and evolution.
Practice Interview
Study Questions
Advanced SQL Fundamentals and Query Optimization
Master complex SQL queries involving multiple joins (INNER, LEFT, RIGHT, FULL OUTER), subqueries, correlated subqueries, window functions, aggregations, CTEs, and analytical functions. Understand query optimization including index usage, query execution plans, join strategies (hash join, sort-merge join, nested loop), statistics, and how to write efficient queries for large datasets. For Staff-level, discuss how to optimize queries for both OLTP and OLAP workloads.
Practice Interview
Study Questions
Onsite Technical Interview 1: Data Modeling and ETL Architecture
What to Expect
This 45-60 minute onsite interview focuses on data modeling for complex real-world scenarios and designing ETL processes that handle massive scale. The interviewer will present a business problem and ask you to design a comprehensive data solution, including warehouse schema, transformation logic, data quality considerations, and operational aspects. For Staff-level candidates, you're expected to discuss architectural decisions that consider scalability, maintainability, cost efficiency, and how the design evolves as the business grows. Questions may involve designing schemas for Amazon's business domains: e-commerce, logistics, advertising, or AWS.
Tips & Advice
Start by clarifying requirements and constraints before designing. Ask questions about data volume, growth rate, query patterns, latency requirements, and business priorities. Draw schemas clearly on the whiteboard and walk through your reasoning step by step. Discuss how you'd handle common data modeling challenges: slowly changing dimensions, fact table granularity, measure columns, and complex joins. Explain your denormalization decisions and justify trade-offs. For ETL, explain your approach to data validation, error handling, recovery, and idempotency. Discuss partitioning strategies for large tables and indexing approaches. Talk about incremental loading vs. full refreshes with business context. For Staff-level, emphasize thinking about maintenance burden, operational complexity, scaling as data volumes grow by 10x, and team capability required to maintain the system. Be prepared to discuss trade-offs explicitly: normalization vs. denormalization, real-time vs. batch, cost vs. complexity, simplicity vs. comprehensiveness.
Focus Topics
Partitioning Strategy and Performance Optimization
Understand table partitioning by date, region, business unit, or other dimensions. Discuss how partitioning affects query performance, data management, and operational overhead. Know when to use clustering, indexing, and materialized views. Understand query optimization in the context of your schema design. Discuss trade-offs between fine-grained partitioning (better query performance) and coarse-grained partitioning (easier management).
Practice Interview
Study Questions
Business Requirements Analysis and Architectural Trade-offs
Gather business requirements from problem statements and translate them into technical solutions. Understand how business questions map to dimensional models and metrics. Make informed trade-offs between denormalization for performance vs. normalization for maintainability, between real-time data vs. batch processing, and between comprehensive data modeling and simplicity. Discuss cost implications.
Practice Interview
Study Questions
ETL Pipeline Design and Data Quality Framework
Design end-to-end ETL pipelines that handle data extraction from multiple sources, complex transformations, validation rules, and error handling. Discuss idempotency, reconciliation, late-arriving facts, and recovery strategies. Understand how to implement data quality frameworks, monitor data quality, alert on issues, and handle data quality problems. For Staff-level, discuss operational monitoring and SLAs.
Practice Interview
Study Questions
Complex Dimensional Data Warehouse Schema Design
Design dimensional models (star schema, snowflake schema, data vault) for complex business scenarios. Handle slowly changing dimensions (Type 1, Type 2, Type 3), bridge tables for many-to-many relationships, degenerate dimensions, conformed dimensions, and junk dimensions. Discuss fact table granularity, additive vs. semi-additive vs. non-additive measures, and aggregate tables. For Staff-level, discuss how schema design evolves as business requirements change.
Practice Interview
Study Questions
Onsite Technical Interview 2: System Design and Big Data Architecture
What to Expect
This 45-60 minute interview focuses on designing large-scale data systems and architectures handling petabyte or exabyte-scale data. You'll be asked to design data pipelines, data lakes, real-time streaming systems, or complete data platforms. The interviewer expects you to consider scalability, fault tolerance, consistency, performance, cost, and operational considerations. For Staff-level candidates, this round assesses your ability to make high-level architectural decisions across the entire data technology stack, considering trade-offs between different technologies and approaches. You may be asked about designing systems that compete globally or handle Amazon's scale.
Tips & Advice
Approach system design problems systematically: understand requirements thoroughly, discuss constraints (scale, latency, consistency, cost), and then propose architecture. Draw system diagrams showing data flow, components, interactions, and technology choices. Justify why you'd choose Spark vs. Flink, Redshift vs. BigQuery, or S3 vs. HDFS based on specific requirements. Consider both batch and real-time paths if applicable. Discuss fault tolerance: how the system handles component failures, data corruption, network partitions, and recovery mechanisms. Address scaling: how the system scales if data volume grows 10x or 100x. Discuss operational aspects: monitoring, alerting, debugging, observability, runbooks for common issues. For Staff-level, emphasize architectural thinking at higher levels of abstraction. Make decisions considering team capability, organizational constraints, cost implications, and long-term flexibility. Discuss how the architecture evolves as the business grows.
Focus Topics
Cost Optimization and Infrastructure Efficiency
Make architectural decisions with cost awareness: compute costs (cluster sizing, auto-scaling), storage costs (data retention, compression, tiering), network costs (data transfer, cross-region replication). Discuss trade-offs between cost and performance. Understand tools for cost monitoring, optimization, and forecasting. Discuss how to right-size infrastructure and handle variable workloads.
Practice Interview
Study Questions
Real-Time and Streaming Data Architecture
Design streaming data pipelines using technologies like Kafka, Kinesis, Flink, or Spark Streaming. Understand event-driven architecture, exactly-once semantics, windowing, and state management. Compare batch vs. streaming approaches and discuss hybrid architectures. Understand when real-time data is necessary vs. near-real-time sufficiency. Discuss backpressure and handling processing delays.
Practice Interview
Study Questions
Big Data Technologies: Spark, Hadoop, and Distributed Computing
Understand Apache Spark architecture (RDDs, DataFrames, Datasets), execution model (driver, executors, tasks, stages), optimization (catalyst optimizer, physical plans). Understand Hadoop ecosystem and when traditional Hadoop is appropriate vs. cloud-native approaches. Know distributed computing concepts: data partitioning, shuffling, data locality, and how they affect performance and cost. Discuss Spark tuning for production workloads.
Practice Interview
Study Questions
AWS Data Services and Technology Stack Selection
Deep understanding of AWS services for data engineering: S3 for data lake storage, Redshift for data warehouse, EMR for big data processing, Glue for serverless ETL, Kinesis for streaming ingestion, Lambda for event-driven processing. Understand when to use each service, their strengths and limitations, cost implications, and how they integrate. Understand AWS networking, security, and compliance considerations for data infrastructure.
Practice Interview
Study Questions
Large-Scale Data Pipeline Architecture and Orchestration
Design end-to-end data pipelines that ingest data from diverse sources (databases, APIs, logs, events), perform transformations at scale, and deliver to multiple destinations. Consider orchestration (Airflow, Step Functions), error handling, monitoring, and SLAs. Discuss batch and streaming architectures, Lambda architecture for combining batch and real-time data, and Kappa architecture as an alternative. Design for reliability and reproducibility.
Practice Interview
Study Questions
Scalability, Fault Tolerance, and Reliability Design
Design systems that scale linearly with data volume while maintaining predictable costs. Implement fault tolerance through redundancy, replication, checksums, and recovery mechanisms. Ensure data consistency across distributed components. Design for operational reliability including comprehensive monitoring, alerting, and automated recovery. Discuss circuit breakers, retries, and backpressure handling.
Practice Interview
Study Questions
Onsite Technical Interview 3: Advanced Problem Solving and Strategic Thinking
What to Expect
This 45-60 minute interview presents complex, real-world data engineering challenges similar to problems Amazon faces. You'll be given ambiguous business problems and asked to think through solutions, trade-offs, implementation strategies, and organizational considerations. This might involve designing solutions for data quality issues at Amazon scale, implementing new business analytics capabilities, or architecting systems for emerging business needs. For Staff-level candidates, this round assesses your ability to navigate ambiguity, make sound strategic decisions with incomplete information, think about organizational readiness, and consider broader implications of technical choices.
Tips & Advice
These problems are intentionally ambiguous to test your thinking process and judgment. Start by asking clarifying questions about requirements, constraints, scale, timeline, success criteria, and business context. Think out loud about trade-offs and alternatives. Discuss how you'd approach building a solution: what you'd do first, what infrastructure you'd use, how you'd validate assumptions, how you'd measure success. Be pragmatic: sometimes the best solution is simpler than the most technically sophisticated approach. For Staff-level candidates, emphasize strategic thinking: considering team capability and organizational readiness, time-to-value, risk management, and long-term maintainability alongside technical optimality. Share examples of how you've handled ambiguous situations and complex organizational decisions using structured thinking. Discuss how you've balanced perfect solutions with pragmatic constraints.
Focus Topics
Technical Leadership and Strategic Technology Decisions
Make informed technology choices at a strategic level: when to build vs. buy, when to use managed services vs. open-source, when to optimize for performance vs. simplicity, when to invest in tooling vs. manual processes. Justify choices considering team skills, organizational constraints, vendor lock-in risks, and future flexibility. Discuss how decisions cascade through the organization.
Practice Interview
Study Questions
Operational Excellence and Production Readiness
Design solutions considering full operational implications: how will this be monitored and alerted on, debugged when failures occur, maintained over time? What happens when it fails? How do you ensure it meets SLAs? Consider observability (logging, metrics, traces), alerting, runbooks, disaster recovery, and automation. Discuss reducing operational burden through automation.
Practice Interview
Study Questions
Data Quality Issues and Troubleshooting at Scale
Handle scenarios involving data quality degradation, unexpected data patterns, missing or duplicate data, or schema drift. Develop systematic approaches to root-cause analysis: where did the problem originate, how early can it be detected, how can recurrence be prevented. Discuss monitoring frameworks, alerting strategies, and data quality SLAs. For Staff-level, discuss how to build data quality culture and responsibilities across teams.
Practice Interview
Study Questions
Handling Ambiguous Requirements and Making Tradeoff Decisions
When given vague business problems, ask clarifying questions to understand true requirements and constraints. Propose solutions that balance technical perfection with pragmatism and business constraints. Discuss trade-offs explicitly and justify recommendations considering cost, time, risk, and team capability. Recommend phased approaches when appropriate.
Practice Interview
Study Questions
Onsite Bar Raiser Interview
What to Expect
The Bar Raiser interview (45-60 minutes) is conducted by an experienced Amazon employee outside your direct chain who evaluates whether you meet or exceed Amazon's leadership standards for Staff-level roles. This interview combines technical questions with deep behavioral exploration, assessing your adherence to Amazon's leadership principles, judgment, and ability to lead and influence others. The Bar Raiser examines whether you set and maintain high standards, mentor effectively, make good decisions under ambiguity, and embody Amazon values. For Staff-level candidates, the Bar Raiser focuses on your strategic thinking, leadership maturity, influence across teams, and ability to elevate organizational capabilities.
Tips & Advice
Prepare stories demonstrating Amazon's leadership principles, especially Customer Obsession, Invent and Simplify, Deliver Results, Are Right, a Lot, and Think Big. Use the STAR method (Situation, Task, Action, Result) for behavioral questions, focusing on your personal agency and decision-making. The Bar Raiser wants to understand how you think, not just what you accomplished. Prepare examples showing: making difficult trade-off decisions and why you chose that path, standing by a position despite disagreement, learning from failures and adjustments made, setting high standards for yourself and others. For Staff-level candidates, stories should demonstrate strategic thinking, mentorship impact on developing senior colleagues, influencing without authority, and raising organizational capabilities. Be ready for technical questions too, and answer them with Bar Raiser-level rigor and depth. Be authentic: Bar Raisers can detect insincerity quickly.
Focus Topics
Mentorship and Enabling Senior Colleagues
Share specific examples of engineers you've mentored—especially senior engineers you've helped grow into Staff-level or other senior roles. Discuss your philosophy on developing talent, creating environments where others succeed, and multiplying your impact through others. Show genuine investment in others' growth. For Staff-level, discuss mentoring other Staff or senior engineers.
Practice Interview
Study Questions
Amazon Leadership Principle: Are Right, a Lot
Discuss how you make good decisions with incomplete information. Share examples of times you were wrong and how you learned. Show intellectual humility, openness to being challenged, and willingness to change your mind. Demonstrate that you seek diverse perspectives and disconfirm your beliefs before deciding. For Staff-level, discuss how you mentor others toward good decision-making.
Practice Interview
Study Questions
Amazon Leadership Principle: Deliver Results
Show your ability to get things done despite obstacles and ambiguity. Share examples of projects you've led to completion under pressure, goals you've achieved while navigating complexity, and accountability you've taken for outcomes. Discuss maintaining focus on business outcomes. For Staff-level, discuss how you've helped teams deliver consistently.
Practice Interview
Study Questions
Technical Excellence and Raising Standards
Discuss how you maintain and raise technical standards in your teams and organization. Share examples of refusing to compromise on code quality, data reliability, or system design. Discuss how you've influenced team standards, coached others toward excellence, and set expectations. For Staff-level, discuss how your standards have shaped organizational practices.
Practice Interview
Study Questions
Amazon Leadership Principle: Customer Obsession
Demonstrate how you make decisions prioritizing customer impact over internal efficiency. Provide examples of championing customer needs, using data to understand customers better, or making trade-offs favoring customer experience. Discuss how you've influenced teams to think customer-first. For Staff-level, discuss how you've shaped organizational approaches to customer focus.
Practice Interview
Study Questions
Amazon Leadership Principle: Invent and Simplify
Demonstrate your ability to drive innovation while keeping solutions simple. Share examples of challenging status quo, innovating within constraints, or simplifying complex problems. Discuss your comfort with experimentation, prototyping, and learning from failures. For Staff-level, discuss how you've inspired innovation in your organization.
Practice Interview
Study Questions
Onsite Hiring Manager Interview
What to Expect
The final onsite interview (45-60 minutes) is typically with the hiring manager, team lead, or potentially a director. This interview assesses cultural fit, team dynamics compatibility, management style alignment, and long-term career fit. The hiring manager wants to understand how you'll integrate with the team, your working style, collaboration approach, and how you'll contribute to team goals and growth. For Staff-level candidates, this conversation also explores your vision for data engineering impact, expectations for scope and influence, and whether you're a good fit for the team's maturity level, current challenges, and future direction.
Tips & Advice
Research the team and hiring manager if possible: their projects, technical challenges, current priorities, team structure, and culture. Come prepared with thoughtful questions about strategic direction, technical challenges, how the team approaches problem-solving, and growth opportunities. Be authentic about your working style, leadership philosophy, and what environment brings out your best work. Listen carefully to how the manager describes challenges and team needs; assess whether you'd thrive and want to contribute. Discuss your leadership style, how you collaborate with peers and reports, and how you balance technical depth with broader organizational concerns. For Staff-level candidates, discuss your vision for data engineering impact in the organization, how you'd mentor senior colleagues, your perspective on balancing technical excellence with business pragmatism, and what you want to learn and accomplish in this role. Ask about the team's technical direction, how they approach innovation, and your potential sphere of influence. Don't oversell; focus on genuine assessment of fit from both directions.
Focus Topics
Amazon Culture Fit and Leadership Principles Resonance
Discuss how Amazon's leadership principles resonate with your personal values and how they've guided your career. Share concrete examples of how these principles have shaped your decisions and approach. Assess whether you're genuinely aligned or discovering misalignment. For Staff-level, discuss how you'd promote these principles in your organization.
Practice Interview
Study Questions
Understanding of Team Context and Strategic Challenges
Demonstrate that you've understood the team's current challenges, priorities, technical strategy, and where they're trying to go. Ask intelligent questions about projects, technical debt, architectural challenges, and how your expertise could help. Show genuine interest in how data engineering contributes to broader business strategy.
Practice Interview
Study Questions
Leadership Philosophy and Team Development Approach
Describe your approach to leadership, mentoring, and team development. Discuss how you create psychological safety, encourage innovation, maintain standards, and handle conflicts. Explain how you develop talent and help senior engineers grow. For Staff-level, discuss your vision for data engineering leadership in the organization.
Practice Interview
Study Questions
Team Fit and Professional Working Style Alignment
Articulate your working style: collaborative vs. independent, structured vs. flexible, detail-oriented vs. high-level thinking. Discuss how you've adapted style to different team contexts and what environment enables your best work. Assess the team's working style and discuss how you'd complement or enhance it. For Staff-level, discuss how you adapt leadership approach to team maturity and organizational context.
Practice Interview
Study Questions
Vision and Strategic Impact at Staff Level
Articulate your vision for how you'd contribute to data engineering at this organization. Discuss what technical or organizational challenges excite you and how you'd approach them. Show that you've thought about Staff-level role beyond individual contribution—influencing strategy, mentoring senior engineers, and shaping organizational capabilities.
Practice Interview
Study Questions
Frequently Asked Data Engineer Interview Questions
Sample Answer
Sample Answer
Sample Answer
from pyspark.sql.functions import expr, floor, rand
N = 10
fact_salted = fact.withColumn("salt", floor(rand()*N))
dim_replicated = dim.crossJoin(spark.range(N).withColumnRenamed("id","salt"))
joined = fact_salted.join(broadcast(dim_replicated), ["key","salt"])Sample Answer
Sample Answer
SELECT
JSON_VALUE(JSON_EXTRACT(payload, '$.level1.level2.attribute')) AS attribute_value
FROM raw_events
WHERE source = 'mysource'
LIMIT 100;SELECT
JSON_EXTRACT_SCALAR(payload, '$.level1.level2.attribute') AS attribute_value
FROM raw_events;SELECT
payload:"level1"."level2"."attribute"::STRING AS attribute_value
FROM raw_events;Sample Answer
Sample Answer
Sample Answer
# Pseudocode (Python-like)
W = window_size # e.g., 1 minute
L = allowed_lateness # allowed lateness
state = {} # map window_key -> {agg, last_emitted, max_event_time_in_window}
max_event_time = -inf
def on_event(event):
global max_event_time
e_ts = event.event_time
max_event_time = max(max_event_time, e_ts)
win_start = floor(e_ts / W) * W
win_key = (event.key, win_start)
s = state.get(win_key, {"agg":0, "last_emitted":None, "max_ts":-inf})
s["agg"] = update_agg(s["agg"], event) # e.g., sum/count
s["max_ts"] = max(s["max_ts"], e_ts)
state[win_key] = s
# Early/progressive trigger (optional): emit provisional every N events or time
if early_trigger_condition(win_key, s):
emit_materialized(win_key, s["agg"], final=False)
s["last_emitted"] = s["agg"]
state[win_key] = s
evaluate_watermarks_and_finalize()
def watermark():
# simple watermark: maximum observed event time minus tolerated skew
return max_event_time - ingest_skew
def evaluate_watermarks_and_finalize():
wm = watermark()
for win_key, s in list(state.items()):
win_end = win_key[1] + W
# Finalization trigger
if wm >= win_end + L:
final_agg = s["agg"]
# If previously emitted and different, emit correction
if s["last_emitted"] is not None and s["last_emitted"] != final_agg:
emit_correction(win_key, old=s["last_emitted"], new=final_agg)
else:
emit_materialized(win_key, final_agg, final=True)
# schedule eviction at win_end + L + GC_DELAY or evict immediately
evict_time = win_end + L + GC_DELAY
schedule_eviction(win_key, evict_time)
def on_timer(evict_key):
# called when eviction timer fires
state.pop(evict_key, None)
# Emit helpers
def emit_materialized(win_key, agg, final):
output = {"window":win_key, "agg":agg, "final":final}
sink(output)
def emit_correction(win_key, old, new):
# Correction event includes original window, previous value and new value
sink({"window":win_key, "correction":True, "old":old, "new":new})Sample Answer
-- Dialect: BigQuery Standard SQL
WITH months AS (
SELECT
DATE_TRUNC(month, dt) AS month_start
FROM UNNEST(
GENERATE_DATE_ARRAY(
DATE_TRUNC(CURRENT_DATE(), MONTH) - INTERVAL 5 MONTH,
DATE_TRUNC(CURRENT_DATE(), MONTH),
INTERVAL 1 MONTH
)
) AS dt
),
mau AS (
SELECT
DATE_TRUNC(event_date, MONTH) AS month_start,
APPROX_COUNT_DISTINCT(user_id) AS mau_approx -- or COUNT(DISTINCT user_id) for exact
FROM `project.dataset.events`
WHERE event_date BETWEEN DATE_TRUNC(CURRENT_DATE(), MONTH) - INTERVAL 5 MONTH
AND LAST_DAY(CURRENT_DATE()) -- partition-pruning friendly
GROUP BY month_start
)
SELECT
m.month_start,
COALESCE(a.mau_approx, 0) AS mau
FROM months m
LEFT JOIN mau a USING (month_start)
ORDER BY m.month_start;Sample Answer
Recommended Additional Resources
- LeetCode: SQL (Medium/Hard difficulty) and Python problems for coding interview preparation
- HackerRank: Data structures, algorithms, and SQL challenges with explanations
- Amazon Careers Page (amazon.jobs) and AWS Data Services documentation for company-specific information
- System Design Interview by Alex Xu: Comprehensive guide to distributed systems design and architecture patterns
- Designing Data-Intensive Applications by Martin Kleppmann: Deep reference for data systems, consistency, and scaling
- Apache Spark: The Definitive Guide by Matei Zaharia and Bill Chambers: Essential for Spark architecture and optimization
- Learning Spark by Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee: Practical Spark programming
- AWS Well-Architected Framework: Best practices for AWS architecture and operational excellence
- The Data Warehouse Toolkit by Ralph Kimball: Dimensional modeling and data warehouse design fundamentals
- Glassdoor and Blind: Real interview experiences and discussions from Amazon candidates and employees
- Amazon Leadership Principles: Study and internalize each principle with concrete examples from your career
- Interview platforms (Exponent, Pramp, Interviewing.io): Practice with experienced engineers and get real feedback
- YouTube: Search for Amazon data engineer interview walkthroughs and system design discussions
- Medium blogs on data engineering: Articles on real-world data challenges and solutions
- AWS data engineering whitepapers: Deep dives into data lake architecture and analytics
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