Apple Business Intelligence Analyst (Entry Level) - Comprehensive Interview Preparation Guide
Apple's BI Analyst interview process for entry-level candidates follows a structured multi-stage evaluation combining phone-based technical screening, a time-boxed take-home case study, and a comprehensive onsite interview loop. The entire process evaluates technical proficiency with SQL and BI visualization tools, analytical problem-solving ability, business acumen, product knowledge, and alignment with Apple's innovation-focused culture. Total duration from initial application to final offer is typically 4-8 weeks.
Interview Rounds
Recruiter Screening
What to Expect
Your initial contact point with Apple (20-30 minutes by phone). A recruiter will verify your professional background, discuss your relevant experience in data analysis or BI, and provide an overview of the BI Analyst role and interview process. The recruiter may assess your enthusiasm for Apple as a company and confirm basic technical background. This call establishes whether you meet minimum qualifications and helps both parties determine mutual fit. The hiring manager may participate to evaluate cultural alignment early.
Tips & Advice
Be authentic and enthusiastic without overselling yourself. Have your resume readily available and be prepared to walk through relevant projects, coursework, or internships involving data analysis or visualization. Articulate a clear understanding of what BI Analysts do - go beyond vague descriptions to specifics about building dashboards, creating reports, and translating data into business decisions. Express genuine interest in Apple's products and the company's mission - mention specific products you use and what attracts you to Apple. Demonstrate learning ability and eagerness to grow in the role. Ask thoughtful questions about the team, typical projects, tools used, and what success looks like in the first 90 days. Research recent Apple announcements or product launches beforehand and reference them naturally to show you follow the company.
Focus Topics
Thoughtful Questions About Role, Team & Career Growth
Prepare specific questions: What does the SDS team focus on? What are typical projects? What tools and technologies will I work with? What does career progression look like? How does the team support junior team members' growth?
Practice Interview
Study Questions
Technical Foundation Overview
Briefly mention any hands-on experience with relevant tools: SQL, Python, Excel, Power BI, Tableau, Looker, or databases. Be specific about what you can do with each tool. Acknowledge gaps honestly - focus on what you're strong at.
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Genuine Interest in Apple Products & Company Mission
Express authentic enthusiasm for Apple - not generic corporate enthusiasm, but genuine interest in specific products, design philosophy, or business achievements. If you're an Apple user, discuss your experience. Reference recent announcements or strategic directions.
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Understanding of BI Analyst Role & Responsibilities
Articulate what you understand about the BI Analyst role beyond a generic job description. Reference specific activities from the job description: building interactive dashboards, creating automated reports, analyzing KPIs, identifying trends, collaborating with stakeholders.
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Professional Background & Relevant Experience
Clearly articulate your educational background, relevant coursework, projects involving data analysis or BI tools, internships, or work experience. Be honest about your skill level while highlighting what makes you a strong candidate despite entry-level status.
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Technical Phone Interview 1 - SQL & Data Fundamentals
What to Expect
A 45-60 minute technical interview conducted via phone with screen-sharing or collaborative coding environment. Focus is on SQL proficiency and fundamental data querying skills. You'll write SQL queries to solve realistic data problems, typically involving SELECT statements, WHERE clauses, JOINs across multiple tables, GROUP BY aggregations, and subqueries. The interviewer will present business scenarios (e.g., 'Find the top 10 customers by revenue' or 'Calculate monthly retention rates') and you'll write queries to answer them. Interviewers evaluate not just correctness but your problem-solving approach, communication of your thinking process, and ability to optimize queries for performance.
Tips & Advice
Before writing code, ask clarifying questions about schema and requirements. Talk through your approach aloud - explain what you're trying to accomplish and why before coding. Use a methodical approach: understand the data structure, identify necessary tables and columns, determine join conditions, write clean readable SQL with aliases and proper formatting. For entry-level, correct logic is more important than optimization, but show awareness of performance. Test your query mentally with example data. If you encounter a syntax issue, reason through it aloud rather than guessing. If stuck, describe your approach step-by-step even if you can't recall exact syntax. Say 'I'm less familiar with that syntax, but I would approach it this way' rather than staying silent. Format code clearly with comments explaining complex sections. Ask the interviewer for confirmation after major sections - 'Does this logic look right to you?'
Focus Topics
Subqueries & Query Optimization
Understand when to use subqueries vs. joins. Grasp basic optimization: writing efficient WHERE clauses, avoiding unnecessary operations, understanding that WHERE filters before aggregation but HAVING filters after. Discuss query performance with larger datasets.
Practice Interview
Study Questions
Problem-Solving Methodology in SQL
Develop systematic approach: clarify requirements, understand schema, break problem into components, write modular queries, verify results with test cases. Verbalize your thinking process clearly.
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Aggregation Functions & GROUP BY
Master COUNT, SUM, AVG, MIN, MAX functions. Understand GROUP BY to create summary statistics by category. Practice complex aggregations like GROUP BY with multiple columns, HAVING clause filtering, and nested aggregations.
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SQL SELECT, WHERE, AND ORDER BY Fundamentals
Master basic SELECT queries retrieving specific columns, filtering rows with WHERE clauses using AND/OR conditions, sorting with ORDER BY, and basic transformations. Understand query execution order and filtering efficiency.
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JOIN Operations (INNER, LEFT, RIGHT, FULL OUTER)
Understand different join types and when to use each. Practice writing multi-table queries combining data from 2-3 tables. Know the difference between INNER JOIN (matching records only), LEFT JOIN (keep all left table records), and FULL OUTER JOIN (keep all records from both tables).
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Technical Phone Interview 2 - BI Tools & Analytics Scenarios
What to Expect
A 45-60 minute second technical phone interview focusing on BI tools, data visualization, and analytical thinking. You'll discuss your experience with BI platforms (Power BI, Tableau, Excel), explain visualization choices for different data types and business questions, and work through analytical scenarios. Some interviews include demonstrating a dashboard you've built or discussing design decisions. You may be asked scenario questions like 'How would you measure app engagement?' or 'Design a dashboard for tracking product launch metrics.' This round evaluates your visualization judgment, understanding of business metrics, communication skills, and ability to translate business questions into analytical approaches.
Tips & Advice
Be honest about your BI tool experience - focus on demonstrating conceptual understanding rather than exaggerating tool expertise. When discussing visualization choices, explain your reasoning: Why this chart type? Who's the audience? What action will they take? Have concrete examples of dashboards you've seen or built. When asked to design a dashboard or analyze a scenario, ask clarifying questions: What's the business objective? Who will use this? What decisions will it support? What data do we have? Discuss KPIs relevant to Apple's business: user engagement, retention, revenue metrics, device adoption, ecosystem lock-in. Think about metrics that matter for products and services. If asked about a tool you're unfamiliar with, explain what you know about similar tools and discuss how you'd approach learning the new tool. Show enthusiasm for learning different BI tools. Discuss data quality challenges and how you'd validate data in a dashboard.
Focus Topics
Translating Business Questions into Analytics
Practice converting vague business questions into specific analytical approaches: Understand what decision the analysis will support. Identify metrics needed. Determine required data and transformations. Design analysis approach. Communicate findings in business context.
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Study Questions
Dashboard Design for Different User Audiences
Understand designing for different users: executive dashboards (high-level trends, key metrics, trends), operational dashboards (real-time monitoring, alerts), analytical dashboards (detailed exploration, drill-downs). Tailor complexity and granularity to audience.
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Data Visualization Principles & Best Practices
Understand visualization fundamentals: matching chart types to data types (bar charts for categorical comparison, line charts for trends over time, scatter plots for relationships), simplicity vs. completeness, avoiding misleading presentations, effective use of color and labeling.
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Study Questions
Key Performance Indicators (KPIs) & Business Metrics
Understand what makes effective KPIs: specific, measurable, actionable, tied to business outcomes. Learn common SaaS metrics (retention, churn, NPS), product metrics (engagement, DAU/MAU), revenue metrics (ARPU, LTV), and Apple-specific metrics (device adoption, ecosystem engagement).
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BI Tool Fundamentals (Power BI, Tableau, Excel)
Demonstrate working knowledge of at least one major BI tool. Understand connecting data sources, building visualizations, creating interactive dashboards, basic formatting, and publishing/sharing. Know when each tool is appropriate and their relative strengths.
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Study Questions
Take-Home Case Study Assignment
What to Expect
After passing phone interviews, you'll receive a case study via email to complete within 72 hours. The case provides a business scenario and dataset, asking you to analyze the data, create visualizations, identify insights, and present recommendations. Examples might include: analyzing customer churn patterns, identifying what drives engagement, evaluating product launch success, or optimizing pricing strategy. You'll deliver a polished presentation (PowerPoint, PDF, or interactive dashboard) that includes an executive summary, detailed analysis methodology, visualizations, key findings, and recommended actions. This round evaluates your end-to-end analytical thinking, data interpretation ability, visualization quality, and communication skills without real-time pressure.
Tips & Advice
Read the case carefully and identify the core business question. Spend initial time exploring the data to understand structure and quality. Create a clear analysis plan before diving into tools. Use a structured approach: Frame the problem, gather relevant data, analyze systematically, create compelling visualizations, draw conclusions, and recommend actions. Create clean, professional visualizations suitable for executive presentation - they should be publication-ready and self-explanatory. Present findings in business terms: 'We're losing 15% of users in month 2' rather than 'p-value of 0.03 indicates statistical significance.' Show your work by documenting methodology, assumptions, and limitations. Time-box your work to 4-6 hours of focused analysis and presentation creation - perfectionism isn't valued. Use clean tools (PowerPoint, Excel, Power BI, Tableau) and focus on communication over fancy formatting. Include clear labels, legends, and data sources on all visualizations. Provide executive summary upfront, then support with detailed findings. Make a recommendation backed by data. If working in Excel/Power BI, document formulas and data transformations clearly.
Focus Topics
Business Problem-Solving & Recommendations
Beyond analyzing data, propose actionable recommendations. Think about business impact and feasibility. Suggest specific next steps or experiments. Show strategic thinking, not just analytical capability.
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Study Questions
Executive Communication & Storytelling
Structure your presentation as a narrative: Start with key conclusions upfront. Provide supporting evidence and visualizations. Explain business implications. Recommend specific actions. Write for non-technical executives - avoid jargon and explain technical concepts clearly.
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Case Study Analysis Framework & Structure
Develop systematic approach: understand business context and core question, explore data structure and quality, identify relevant metrics and relationships, form analytical questions, conduct analysis, draw conclusions. Document assumptions and limitations clearly.
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Data Exploration & Interpretation
Explore data distribution, identify outliers and anomalies, understand data quality issues, segment data to identify patterns. Move from descriptive analysis (what happened) to insight generation (why it matters). Ask 'So what?' after each finding.
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Professional Visualization & Dashboard Design
Create clean, publication-ready visualizations with clear titles, labeled axes, consistent formatting, and appropriate color schemes. Choose chart types that best communicate findings. Ensure every visualization directly supports your conclusions.
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Onsite Interview 1 - Data Analysis & Visualization Deep Dive
What to Expect
First of your onsite interviews (60 minutes) with a BI team member or senior analyst. Expect discussion of your take-home case study, including deep questions about your methodology, alternative approaches considered, and what you'd do differently with more time or data. You may also solve a real-world data analysis problem or design a dashboard for a specific business scenario. The interviewer probes your technical depth: how you handle data quality issues, validate findings, perform exploratory analysis, and communicate insights to different stakeholders. This round evaluates hands-on analytical capability with real, messy data.
Tips & Advice
Come thoroughly prepared to discuss your case study - be ready to explain methodology, discuss tradeoffs you made, talk about alternative analyses you considered, and articulate what insights you prioritized and why. When presented with a new data problem, clarify requirements before jumping into analysis: What's the business question? Who's the audience? What data is available? Work through the problem systematically, explaining your thinking aloud. Discuss how you'd validate your analysis: 'Does this number make sense?' Proactively surface assumptions and limitations: 'I'm assuming this represents all users globally - if regional differences matter, we should segment by region.' Show interest in learning - if the interviewer suggests a different approach, ask why they'd choose it and what advantages it offers. Demonstrate growth mindset by discussing what you'd do differently with more experience. Ask follow-up questions: 'How would this approach change if the dataset were 10x larger?'
Focus Topics
Performance & Scalability Considerations
Discuss how your analyses scale: query performance with large datasets, dashboard refresh time and latency, infrastructure constraints, and optimization tradeoffs. Show thinking about practical constraints.
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Study Questions
Stakeholder Communication Across Technical & Business Groups
Practice adapting communication for different audiences: technical colleagues (discuss methodology, data sources), business stakeholders (focus on business implications), executives (highlight key takeaways and recommendations).
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Data Quality, Validation & Data Issues
Understand common data quality problems: missing values, duplicates, inconsistent formatting, outliers, incorrect definitions. Discuss identifying data issues, validating data, documenting assumptions, communicating data limitations to stakeholders.
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Advanced Data Analysis Techniques & Methods
Move beyond basic aggregations: trend analysis and time-series patterns, forecasting basics, anomaly detection, cohort analysis, statistical concepts. Practice identifying patterns in complex datasets and extracting deeper insights.
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Dashboard Design from Business Requirements
Take a business scenario and design complete dashboard: identify appropriate metrics, determine layout and hierarchy, choose visualization types, consider interactivity needs, design for user workflow. Create wireframes or mockups.
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Onsite Interview 2 - Product Metrics & Business Analytics
What to Expect
A 60-minute interview with a product-focused team member, product manager, or business analyst. Focus is on product metrics, business analytics, and strategic thinking. You'll discuss how to measure product success, identify relevant KPIs for business questions, understand user behavior through data, and think about analytics supporting product decisions. Example questions: 'How would you measure the success of a new Apple Watch fitness feature?' or 'What metrics would indicate declining iPhone sales?' or 'Design the metrics dashboard for measuring App Store health.' This round evaluates business acumen alongside technical skills - understanding what metrics matter for strategy.
Tips & Advice
Research Apple's products deeply before the interview: current product lines (iPhone, Mac, iPad, Watch, Services), recent announcements, product strategy, competitive positioning. Think about how you'd measure success differently for hardware vs. services. For any metric question, clarify what business decision the metric will support - metrics without decision context are meaningless. Discuss trade-offs: why this metric instead of alternatives? What doesn't this metric tell us? Show understanding that metrics are tied to strategy. Mention common product metrics: DAU (daily active users), retention rates, engagement time, feature adoption, churn, lifetime value. Show understanding of how Apple's ecosystem creates network effects. Discuss ethical considerations in metrics design. Ask thoughtful questions about the team's approach to measurement. If asked about Apple's internal metrics (which you can't know), discuss what you'd measure and why, showing analytical thinking.
Focus Topics
Strategic Insights from Data
Practice identifying business opportunities, threats, and emerging trends in data. Think strategically: What patterns matter for company strategy? Where are unexplored opportunities? What should we investigate further?
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Cohort Analysis & User Behavior Understanding
Master cohort analysis fundamentals: segmenting users by signup date or characteristics, tracking behavior across cohorts, measuring retention curves, comparing cohort performance over time, identifying patterns in user lifecycle.
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A/B Testing & Experimentation Foundations
Understand basics of A/B testing: forming hypotheses, experimental design, randomization, measuring effect size, statistical significance, and interpreting results. Know when experimentation is appropriate vs. observational analysis.
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Product Metrics & Success Measurement
Understand core product metrics: engagement (DAU, MAU, session length, feature usage), retention (retention curves, churn rate), conversion (signup-to-paid, funnel metrics), monetization (ARPU, lifetime value), and quality (crash rates, latency). Practice selecting appropriate metrics for different business questions.
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Apple Business Model & Product Strategy
Deeply research Apple's business: main revenue sources (hardware sales, services, App Store), product categories and their role in strategy, ecosystem strategy and lock-in, geographic markets, pricing strategy. Understand competitive positioning.
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Onsite Interview 3 - BI Systems & Architecture
What to Expect
A 60-minute technical interview with a BI engineer, analytics engineer, or data engineer covering BI infrastructure and system design. You'll discuss how data flows from operational systems to analytics platforms, understand ETL/ELT processes conceptually, discuss data warehouse architecture, and explore tool selection and integration. Questions might cover: 'How would you design a data pipeline for real-time dashboards?' or 'Explain how our dashboards stay current with fresh data' or 'What are tradeoffs between different BI tools?' This round evaluates understanding of the technical ecosystem supporting analytical work, systems thinking, and ability to work with data infrastructure teams.
Tips & Advice
You're not expected to be a data engineer, but show conceptual understanding of data systems. Understand these core concepts: data sources (operational databases, APIs, logs), ETL/ELT processes (transforming data for analytics), data warehouses (optimized for analytical queries), and BI tools connecting to data. Know that different approaches have tradeoffs: batch processing is cost-effective but less fresh, real-time streaming is fresh but more complex. Be familiar with different BI tool architectures - understand that Power BI, Tableau, and Looker work differently. Show curiosity about Apple's data infrastructure. Ask questions: 'How is data collected from different Apple systems?' 'What's the data refresh cadence for dashboards?' 'What data quality checks exist?' If you encounter unfamiliar concepts, acknowledge the gap but reason through the problem: 'I haven't worked with that specifically, but I'd approach it this way.' Show interest in learning how systems work.
Focus Topics
Data Quality, Governance & Metadata
Understand data quality frameworks, metadata management (documenting what data means), data governance policies, and lineage (tracking data origin through transformations).
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Study Questions
Scalability & Performance in Analytics Systems
Discuss how analytical systems scale: query performance with large datasets, dashboard load times, refresh cycles, infrastructure requirements, and optimization strategies. Understand database indexing and query plan basics.
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BI Tool Architecture & Platform Differences
Understand different BI tool architectures: in-memory models vs. direct query, caching strategies, connection to data sources, refresh cadences, handling concurrent users. Discuss strengths/limitations of different tools.
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ETL/ELT & Data Pipeline Concepts
Understand Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT) at conceptual level. Know differences between batch and real-time pipelines, understand data freshness tradeoffs, recognize common challenges in data movement, discuss orchestration tools conceptually.
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Data Warehouse Architecture & Dimensional Design
Understand data warehouse fundamentals: OLTP vs. OLAP, fact and dimension tables, star schema, denormalization for analytical queries, slowly changing dimensions. Understand data lakes and when they're used as alternatives.
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Onsite Interview 4 - Behavioral & Cultural Alignment
What to Expect
A 60-minute behavioral interview typically with your potential manager or a senior team member evaluating fit with Apple's culture and working style. You'll discuss past experiences demonstrating problem-solving, teamwork, communication, learning ability, and handling ambiguity. Questions use the STAR method (Situation, Task, Action, Result): 'Tell me about a time you solved a complex problem' or 'Describe a situation where you had to learn something new quickly.' This round assesses soft skills, emotional intelligence, collaboration style, growth mindset, and genuine interest in Apple. Success here depends on authentic, well-prepared stories and demonstrating alignment with innovation and excellence values.
Tips & Advice
Prepare 6-8 concrete stories from your background - academic projects, internships, volunteer work, personal projects. Each story should demonstrate a key competency: analytical thinking, teamwork, learning agility, handling ambiguity, communication, or user empathy. Use the STAR framework: Situation (context, briefly), Task (your responsibility), Action (specific steps YOU took), Result (outcome with data/metrics when possible). Practice telling stories concisely - each should take 2-3 minutes. Use specific examples rather than generalizations: 'I improved query performance by 40%' beats 'I optimized queries.' Discuss genuine passion for Apple products - if you're a user, authentically discuss your experience. Listen carefully to questions and answer what's asked, not what you prepared. Ask clarifying questions if needed. Show humility by discussing failures and what you learned. Emphasize learning ability and growth mindset: 'I didn't know X initially, so I...' Demonstrate how you work with diverse teams. Have 2-3 thoughtful questions about team dynamics, mentorship, and career development.
Focus Topics
Handling Ambiguity & Managing Competing Priorities
Share stories about managing situations with unclear requirements, competing deadlines, or conflicting feedback. Discuss how you clarified requirements, prioritized work, communicated status, and adapted approaches.
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Study Questions
Learning Ability & Growth Mindset
Discuss situations where you quickly learned new tools, frameworks, or domains. Share your learning approach, resources you use, and specific skills developed. Show enthusiasm for continuous learning and growth.
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Passion for Apple Products & Company Mission
Authentically discuss why you're excited about Apple - specific products you love, design philosophy you admire, business achievements that impress you. Share personal experience as Apple user if applicable.
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Teamwork & Cross-Functional Collaboration
Share examples of successfully working with diverse teams: technical colleagues, business stakeholders, managers. Discuss communication approach, handling differing perspectives, resolving conflicts, supporting team goals over individual goals.
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Problem-Solving & Analytical Thinking
Tell stories demonstrating how you approach complex problems: breaking problems into components, gathering information, considering multiple approaches, making tradeoffs, and implementing solutions. Discuss situations with incomplete information or competing requirements.
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Frequently Asked Business Intelligence Analyst Interview Questions
Sample Answer
WITH raw AS (
SELECT
sale_date,
SUM(amount) AS raw_total
FROM staging.sales
GROUP BY sale_date
),
wh AS (
SELECT
sale_date,
total_amount AS wh_total
FROM warehouse.fact_sales
)
SELECT
COALESCE(r.sale_date, w.sale_date) AS sale_date,
COALESCE(r.raw_total, 0) AS raw_total,
COALESCE(w.wh_total, 0) AS wh_total,
ROUND(ABS(COALESCE(r.raw_total,0) - COALESCE(w.wh_total,0)), 2) AS abs_diff,
-- percent diff relative to warehouse value; if wh_total = 0 and raw_total > 0 treat as 100%
ROUND(
CASE
WHEN NULLIF(w.wh_total, 0) IS NULL AND COALESCE(r.raw_total,0) = 0 THEN 0
WHEN NULLIF(w.wh_total, 0) IS NULL AND COALESCE(r.raw_total,0) <> 0 THEN 100
ELSE 100.0 * ABS(COALESCE(r.raw_total,0) - w.wh_total) / NULLIF(w.wh_total, 0)
END
, 2) AS pct_diff
FROM raw r
FULL OUTER JOIN wh w
ON r.sale_date = w.sale_date
WHERE
-- flag rows where abs difference > 1000 OR percent difference > 1%
(
ABS(COALESCE(r.raw_total,0) - COALESCE(w.wh_total,0)) > 1000
OR
(
CASE
WHEN NULLIF(w.wh_total, 0) IS NULL AND COALESCE(r.raw_total,0) = 0 THEN 0
WHEN NULLIF(w.wh_total, 0) IS NULL AND COALESCE(r.raw_total,0) <> 0 THEN 100
ELSE 100.0 * ABS(COALESCE(r.raw_total,0) - w.wh_total) / NULLIF(w.wh_total, 0)
END
) > 1
)
ORDER BY sale_date;Sample Answer
SELECT
week_start,
total_revenue,
rolling_median,
CASE
WHEN rolling_median IS NULL THEN 'insufficient_history'
WHEN ABS(total_revenue - rolling_median) / NULLIF(rolling_median,0) > 0.30 THEN 'anomaly'
ELSE 'ok'
END AS anomaly_flag,
ROUND(ABS(total_revenue - rolling_median) / NULLIF(rolling_median,0), 3) AS pct_change
FROM (
SELECT
week_start,
total_revenue,
-- 4-week rolling median of the *previous* 4 weeks (exclude current week)
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY total_revenue)
OVER (ORDER BY week_start
ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS rolling_median
FROM weekly_revenue
) t
ORDER BY week_start;Sample Answer
Sample Answer
-- 1) hourly conversion rate
SELECT date_trunc('hour', event_time) as hour,
COUNT(DISTINCT CASE WHEN event='purchase' THEN user_id END) as purchases,
COUNT(DISTINCT user_id) as users,
COUNT(*) FILTER (WHERE event='session_start') as sessions,
(COUNT(DISTINCT CASE WHEN event='purchase' THEN user_id END)::float / NULLIF(COUNT(DISTINCT user_id),0)) as conv_rate
FROM events
WHERE event_time >= now() - interval '14 days'
GROUP BY 1
ORDER BY 1;-- 2) referrals completeness
SELECT date(event_time) as day,
COUNT(*) as sessions,
SUM(CASE WHEN referral IS NULL OR referral = '' THEN 1 ELSE 0 END) as missing_referrals,
100.0 * SUM(CASE WHEN referral IS NULL OR referral = '' THEN 1 ELSE 0 END) / COUNT(*) as pct_missing
FROM sessions
WHERE event_time >= now() - interval '14 days'
GROUP BY 1
ORDER BY 1;-- 3) funnel breakdown by channel
SELECT channel,
COUNT(DISTINCT session_id) FILTER (WHERE event='landing') as landings,
COUNT(DISTINCT session_id) FILTER (WHERE event='add_to_cart') as atc,
COUNT(DISTINCT session_id) FILTER (WHERE event='purchase') as purchases,
100.0 * COUNT(DISTINCT session_id) FILTER (WHERE event='purchase') / NULLIF(COUNT(DISTINCT session_id) FILTER (WHERE event='landing'),0) as conv_from_landing
FROM events
WHERE event_time >= now() - interval '14 days'
GROUP BY channel
ORDER BY conv_from_landing DESC;Sample Answer
CREATE TABLE IF NOT EXISTS analytics.agg_daily_sales (
region STRING,
category STRING,
day DATE,
total_amount NUMERIC,
total_orders INT64
)
PARTITION BY day
CLUSTER BY region, category;MERGE analytics.agg_daily_sales T
USING (
SELECT
region,
category,
DATE(order_ts) AS day,
SUM(amount) AS total_amount,
COUNT(1) AS total_orders
FROM raw.orders
WHERE DATE(order_ts) = CURRENT_DATE() -- only today's data
GROUP BY region, category, day
) S
ON T.region = S.region AND T.category = S.category AND T.day = S.day
WHEN MATCHED THEN
UPDATE SET total_amount = S.total_amount, total_orders = S.total_orders
WHEN NOT MATCHED THEN
INSERT (region, category, day, total_amount, total_orders)
VALUES (S.region, S.category, S.day, S.total_amount, S.total_orders);Sample Answer
Sample Answer
models:
- name: fact_sales
columns:
- name: sale_id
tests: [unique, not_null]
- name: sale_date
tests: [not_null]
- name: amount
tests:
- not_null
- accepted_values: {min_value: 0}-- tests/null_rate_limit.sql
select *
from {{ model }}
where (sum(case when {{ column_name }} is null then 1 else 0 end) over ())::float / count(*) > {{ severity_threshold }}select count(*) as total,
sum(case when customer_id is null then 1 else 0 end) as nulls,
sum(case when customer_id is null then 1 else 0 end)/count(*) as null_rate
from analytics.fact_salesselect src.cnt as src_cnt, tgt.cnt as tgt_cnt, tgt.cnt - src.cnt as diff
from (select count(*) as cnt from source.sales where sale_date = '{{ ds }}') src,
(select count(*) as cnt from analytics.fact_sales where sale_date = '{{ ds }}') tgt;tests:
- relationships:
to: ref('dim_customer')
field: customer_idselect count(*) from analytics.fact_sales f
left join dim_customer c on f.customer_id = c.customer_id
where c.customer_id is null;with baseline as (
select avg(total_amount) avg_amt, stddev(total_amount) std_amt
from analytics.daily_sales where sale_date between '{{ ds_minus_14 }}' and '{{ ds_minus_1 }}'
)
select d.sale_date, d.total_amount,
(case when d.total_amount between avg_amt - 3*std_amt and avg_amt + 3*std_amt then 'ok' else 'alert' end) as status
from analytics.daily_sales d cross join baseline
where d.sale_date = '{{ ds }}';-- hash-based diff
select count(*) as mismatch_count
from (
select md5(concat_ws('|', sale_id, amount, customer_id)) as h from source.sales where sale_date='{{ ds }}'
) s
full outer join (
select md5(concat_ws('|', sale_id, amount, customer_id)) as h from analytics.fact_sales where sale_date='{{ ds }}'
) t using (h)
where s.h is null or t.h is null;Sample Answer
Sample Answer
Sample Answer
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