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Role-Level Learning Path

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E3

E3 — Data Analyst

Suggested timeline: 6–8 weeks

Entry-level analytical role. Focus on clean SQL, descriptive statistics, data storytelling, and translating business questions into queries.

Who this is for: Graduating students, career changers, analysts with <2 years experience targeting IC3/E3 roles at Meta, Google, or comparable companies.

What to Expect in the Interview Loop

1. SQL coding screen (30–45 min, medium SELECT/JOIN/GROUP BY)
2. Case study — define a metric, interpret a chart
3. Behavioral — tell me about a project you worked on

Key Skills to Master

SQL — Core Queries

Challenge bar: 🟢 Easy — Medium

• SELECT, WHERE, ORDER BY, LIMIT
• GROUP BY + aggregate functions (COUNT, SUM, AVG, MAX, MIN)
• INNER JOIN, LEFT JOIN across 2 tables
• Subqueries for simple filtering
• Basic date functions (DATE_DIFF, DATE_TRUNC)

Python — Exploratory Analysis

• pandas: read_csv, head, describe, value_counts
• DataFrame selection, filtering, sorting
• groupby + aggregation
• Basic matplotlib / seaborn charts
• Handling missing values with fillna / dropna

Statistics — Descriptive

• Mean, median, mode — when to use each
• Variance and standard deviation
• Distributions: normal, skewed, bimodal
• Correlation vs causation
• Percentiles and outlier detection

Product Sense — Fundamentals

• Defining a North Star metric for a simple product
• Interpreting a drop in a metric (basic)
• User funnel basics (awareness → activation → retention)

Your Phased Study Plan

Phase 1 — Foundations (Weeks 1–2)

1–1.5 hours/day

Focus: Statistics and core SQL

• Work through Statistics & Probability module — focus on descriptive stats
• Complete SQL Basics: SELECT, WHERE, GROUP BY, JOINs
• Practice 3 Easy SQL problems on LeetCode or StrataScratch daily
• Read the SQL Cheat Sheet; reproduce every example by hand

Phase 2 — Analysis Skills (Weeks 3–4)

1.5–2 hours/day

Focus: Python EDA and metric thinking

• Python module: pandas groupby, merge, pivot
• Build a simple EDA notebook on a Kaggle dataset
• Practice defining a North Star + 3 supporting metrics for a familiar app
• Work through 2 beginner analytical execution case studies

Phase 3 — Interview Simulation (Weeks 5–8)

2 hours/day

Focus: Timed practice and behavioral prep

• Timed SQL: solve 2 Medium problems in <15 min each
• Write and refine 5 STAR stories using the Behavioral Interview guide
• Complete the Interview Day Checklist
• Do 2 mock analytical case studies with a study partner

Curated Handbook Pages for E3

Introduction & Interview Overview Statistics & Probability SQL & Data Manipulation Python for Data Analysis Beginner Exercises Technical Skills Interview Analytical Execution Behavioral Interview SQL Cheat Sheet Statistics Cheat Sheet Interview Day Checklist

E4

E4 — Senior Analyst / Data Scientist

Suggested timeline: 4–6 weeks

Mid-level individual contributor. Owns analysis end-to-end, designs and interprets A/B tests, and partners cross-functionally to influence product decisions.

Who this is for: 2–5 years experience. Analysts targeting IC4/E4 at Meta, Google, Airbnb, or data science roles at growth-stage companies.

What to Expect in the Interview Loop

1. SQL coding screen (45 min, window functions, CTEs, multi-table joins)
2. A/B testing — design an experiment, interpret results, flag pitfalls
3. Product sense — define metrics, diagnose a metric drop
4. Behavioral — impact, collaboration, data-driven decision making

Key Skills to Master

SQL — Window Functions & CTEs

Challenge bar: 🟡 Medium

• Window functions: ROW_NUMBER, RANK, LAG, LEAD
• CTEs for multi-step logic
• Self-joins for sequential event analysis
• CASE WHEN for conditional aggregation
• Date spine / calendar table patterns

Python — Production-Ready ETL

• pandas: merge, pivot_table, apply, lambda
• Cohort retention matrix with groupby
• Basic regex for text field parsing
• scipy.stats for t-test and chi-square
• Clean, reusable function design

Statistics — Inferential

• Hypothesis testing: null/alternative, p-value, alpha
• Two-sample t-test and proportion z-test
• Statistical power and sample size calculation
• Type I vs Type II errors
• Confidence intervals and effect sizes

Product Sense — Metrics & Experiments

• North Star + Leading + Guardrail metric tiers
• A/B test design: unit of randomization, SRM checks
• Novelty effects and user bias
• Root cause analysis: 4-layer framework
• Funnel analysis and cohort retention

Your Phased Study Plan

Phase 1 — Technical Refresh (Days 1–7)

1.5–2 hours/day

Focus: Window functions and inferential statistics

• SQL module: window functions — write 10 queries using ROW_NUMBER, LAG, LEAD
• Statistics module: hypothesis testing and confidence intervals
• Solve 5 Medium SQL problems on LeetCode (Database) under timed conditions
• Read Advanced SQL Patterns & Techniques supplementary guide

Phase 2 — Analytical Depth (Days 8–21)

2–2.5 hours/day

Focus: A/B testing, product sense, Python ETL

• A/B Testing module: sample size calc, SRM, novelty effects
• Python: build a cohort retention matrix from scratch
• Practice 3 analytical execution case studies with the SPSIL framework
• Define metrics for 2 unfamiliar products (pick from app stores)
• Complete the 21-Day Sprint Days 8–14 exercises

Phase 3 — Interview Simulation (Days 22–42)

2 hours/day

Focus: Mock interviews and behavioral polish

• Timed SQL: 5 Medium + 2 Hard problems per week
• Write 8 STAR stories — ensure each covers a Meta value
• Practice the 4-layer RCA framework on 3 real anomaly scenarios
• Complete behavioral mock interview guide
• Review SQL Interview Problems — all 15 problems with self-grading

Curated Handbook Pages for E4

Statistics & Probability SQL & Data Manipulation A/B Testing & Experimentation Python for Data Analysis Intermediate Exercises Technical Skills Interview SQL Interview Problems (15 Curated) Analytical Execution Analytical Reasoning / Product Sense Behavioral Interview 21-Day Sprint Guide Advanced SQL Patterns Sample Size Calculator

E5

E5 — Senior Data Scientist

Suggested timeline: 6–8 weeks

Senior IC. Drives complex multi-team experiments, mentors junior analysts, designs measurement frameworks, and contributes to technical architecture decisions.

Who this is for: 5+ years experience. Targeting IC5/E5/Senior DS at Meta, Google, or Staff/Lead Analyst at companies like Stripe, Shopify, or Airbnb.

What to Expect in the Interview Loop

1. SQL coding screen (60 min, optimization, complex window functions, performance reasoning)
2. Advanced A/B testing — causal inference, network effects, switchback experiments
3. Product sense — system-level metric design, trade-off analysis
4. Analytical execution — ambiguous open-ended case, structure and present findings
5. Cross-functional leadership — behavioral + influence stories

Key Skills to Master

SQL — Optimization & Scale

Challenge bar: 🔴 Hard

• Query optimization: EXPLAIN, index usage, predicate pushdown
• Sessionization with LAG + cumulative SUM patterns
• Multiple CTEs chained for complex multi-step pipelines
• Approximate aggregations (APPROX_COUNT_DISTINCT)
• Performance trade-offs: broadcast vs shuffle joins

Python — Statistical & Scalable

• Difference-in-differences implementation
• Propensity score matching for observational studies
• Custom A/B test variance reduction (CUPED)
• Recursive JSON flattening and log parsing
• Scalability discussion: from pandas to Spark

Statistics — Causal Inference

• Causal inference: RCT vs quasi-experiments
• Difference-in-differences (DiD)
• Propensity score matching (PSM)
• CUPED / variance reduction
• Multiple testing corrections (Bonferroni, FDR)
• Network effects and interference

Product Sense — Systems Thinking

• Metric ecosystem design for a whole product area
• Experiment trade-offs: speed vs accuracy vs power
• Long-term vs short-term metric misalignment
• Stakeholder alignment on ambiguous goals
• Identifying leading vs lagging indicators at scale

Your Phased Study Plan

Phase 1 — Advanced Technical (Weeks 1–2)

2 hours/day

Focus: Hard SQL, causal inference, scalable Python

• SQL: solve 5 Hard problems per week; annotate each with performance notes
• Statistics: implement DiD and PSM in Python from scratch
• Python: build a recursive log parser and a stateful event validator
• Read and implement the Advanced SQL Patterns & Techniques module

Phase 2 — Strategic Depth (Weeks 3–5)

2–2.5 hours/day

Focus: Complex experimentation and metric design

• A/B testing: implement CUPED variance reduction on a sample dataset
• Design a metric framework for a multi-sided marketplace
• Practice 4 hard analytical execution cases with written outputs
• Work through the network effects experiment design section
• Simulate a data-driven pushback conversation with a PM

Phase 3 — Leadership & Communication (Weeks 6–8)

1.5–2 hours/day

Focus: SPSIL stories, cross-team influence, and architectural awareness

• Write 3 SPSIL stories for cross-functional technical projects
• Prepare a metric retrospective: what would you change in a past project?
• Practice presenting complex findings to a non-technical audience in <5 minutes
• Review data engineering best practices (strategy + architecture)

Curated Handbook Pages for E5

Statistics & Probability A/B Testing & Experimentation Advanced SQL (PostgreSQL) Advanced Exercises Technical Skills Interview Analytical Execution Analytical Reasoning / Product Sense Behavioral Interview Advanced SQL Patterns & Techniques Analytical Patterns Reference Statistics & Probability Practice Data Strategy & Architecture Example Analytical Execution

E6

E6 — Staff Data Engineer / Staff Data Scientist

Suggested timeline: 8–12 weeks

Staff-level IC. Acts as a technical force multiplier. Designs data systems at exabyte scale, drives architectural decisions, and leads through influence across multiple teams.

Who this is for: 8+ years experience. Targeting IC6/E6/Staff at Meta, Google, or Principal/Staff Eng at top-tier companies.

What to Expect in the Interview Loop

1. SQL velocity screen (30 min, 5 problems, exabyte-scale optimization)
2. Python ETL — productionalization, scalability, state management
3. Data modeling — grain, SCD, Star vs OBT, mini-dimensions
4. Distributed systems design — Lambda/Kappa/Medallion, exactly-once
5. Product sense + metrics — North Star, RCA framework
6. Leadership & ownership — SPSIL (Situation, Problem, Solution, Impact, Lessons) stories, architectural defense

Key Skills to Master

SQL — Exabyte-Scale Velocity

Challenge bar: 🔴 Hard (5 problems in 30 min)

• Partition pruning on ds column — always filter before any JOIN
• Salting skewed keys for hot-partition mitigation
• Broadcast join hints for small dimension tables
• Sessionization: LAG + cumulative SUM session ID pattern
• COALESCE, CAST — production guardrails
• EXPLAIN plan reasoning — state out loud

Python — Production ETL

• collections.Counter / defaultdict for O(1) aggregation
• Log parsing with re.compile (named groups)
• Recursive JSON flattening with max_depth guard
• State-machine event sequence validation
• 100× scalability: pandas → Spark / Kafka → Redis for state

Data Modeling — Architectural

• Grain definition before any table design
• Fact table types: Transactional, Periodic Snapshot, Accumulating
• SCD Type 1 vs Type 2 trade-off defense
• Star schema vs One Big Table (OBT)
• Mini-dimensions for rapidly changing attributes
• Bridge tables for many-to-many with weighting factors

Distributed Systems — Architecture

• Lambda vs Kappa vs Medallion architecture trade-offs
• Exactly-once via at-least-once + idempotent MERGE
• Log-based CDC (Debezium) vs query polling
• Backpressure, schema drift, small-file problem
• Reprocessing strategy and data lineage

Your Phased Study Plan

Phase 1 — SQL & Python Velocity (Weeks 1–2)

2–2.5 hours/day

Focus: Exabyte-scale SQL and production ETL patterns

• SQL: 5 Hard problems per day, timed at 6 min each — no IDE
• Annotate each SQL solution with partition pruning and JOIN strategy
• Python: implement log parser, JSON flattener, state validator from the E6 guide
• Write a 100× scale discussion for each Python exercise

Phase 2 — Architecture Mastery (Weeks 3–5)

2.5 hours/day

Focus: Data modeling and distributed systems

• Data modeling: design 3 star schemas from scratch — state grain first
• Implement SCD Type 2 logic in SQL (MERGE INTO with effective/expiry dates)
• Design a full Medallion pipeline for one of the system design scenarios
• Write an architectural comparison doc: Lambda vs Kappa for your domain
• Review the Staff Data Engineer (E6) six-module syllabus thoroughly

Phase 3 — Product Sense & Leadership (Weeks 6–8)

2 hours/day

Focus: Metrics framework and cross-team influence

• Define North Star + Leading + Guardrail metrics for 3 Meta products
• Practice the 4-layer RCA framework — write out SQL for each layer
• Prepare 4 SPSIL stories with architectural alternatives explicitly stated
• Conduct a granular project retrospective: what broke, why, what changes

Phase 4 — Mock & Polish (Weeks 9–12)

2–3 hours/day

Focus: Full-loop simulation and gap remediation

• Run a full mock interview loop (all 6 rounds) with a peer
• Timed SQL: 5 problems in 30 minutes — plain text, no autocomplete
• Architectural defense: have a peer probe your past systems for weaknesses
• Review the interview-day strategy table in the E6 syllabus

Curated Handbook Pages for E6

⭐ Staff Data Engineer (E6) Six-Module Syllabus Technical Skills Interview SQL Interview Problems (15 Curated) Advanced SQL (PostgreSQL) Advanced SQL Patterns & Techniques Data Strategy & Architecture Data Architecture Best Practices Advanced Exercises Analytical Reasoning / Product Sense Behavioral Interview (SPSIL + Leadership) Analytical Patterns Reference Meta Specificity & Culture

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