Focus areas top companies probe

  • SQL and data-manipulation live coding (Meta, Google, Netflix emphasize this heavily).
  • General coding (Python/Java) including DS/algorithms for data tasks and script-like problems.
  • Data modeling, data warehousing, batch/streaming pipelines and distributed systems design.
  • Product and analytics sense: defining metrics, experimentation, interpreting results (especially Meta, Netflix, OpenAI).
  • Senior-level leadership and ownership; culture fit (Meta ownership, Googleyness, OpenAI/Anthropic safety and mission focus).

This week’s deliverable: write a one-page “target candidate” profile (skills, leadership stories, impact examples) that you’d hire for these roles, and compare yourself honestly against it.

1. Understand each company’s loop (1–2 days)

You don’t want generic prep; tailor to real loops:

  • Meta: recruiter screen → technical screen (heavy SQL + some coding) → onsite with ~3 technical (SQL, data modeling, product/data case) + 1 behavioral/ownership interview.
  • Netflix: interviews emphasize system design for data products, pipelines, SQL, distributed processing, and pragmatic tradeoffs for metrics and recommendations.
  • Google: screens plus onsite focused on general cognitive ability, role-related knowledge (SQL, coding, distributed systems), and Googleyness/leadership.
  • OpenAI: recruiter screen → technical screen (often including a take-home and review) → 4–6 hour virtual onsite with data modeling, pipeline design, system design, and behavioral interviews; senior roles get extra architecture deep dives.
  • Anthropic: multi-step process with recruiter call, online coding/assessment, hiring manager screen, then 4–5 technical loops plus values/ethics discussions (safety, long-term impact).

Deliverable: a 1-page “loop map” listing for each company: rounds, focus, and how your experience maps to each.

2. 12-week roadmap (high level)

Overlapping, rolling prep while you network and start processes:

  • Weeks 1–2: Baseline assessment; SQL and coding diagnostics; refresh core DE fundamentals; draft stories.
  • Weeks 3–4: Double down on SQL and coding; start system-design drills; refine projects into strong narratives.
  • Weeks 5–6: Advanced data modeling/pipelines; product/metrics and experimentation; mock interviews.
  • Weeks 7–8: Company-specific drills (Meta/Netflix/Google first); refine behavioral stories; start real recruiter screens.
  • Weeks 9–10: OpenAI/Anthropic-style deep-dives; architecture sessions; mission/values articulation.
  • Weeks 11–12: Full mock loops, weak-spot patching, offer strategy and negotiation prep.

Assume 10–12 focused hours/week; adjust up or down as needed.

3. Weekly breakdown with concrete work

Weeks 1–2: Baseline and fundamentals

  1. Diagnostics
    • Take timed SQL and Python/SQL mixed assessments similar to Meta/Google screens (e.g. complex joins, window functions, case-style queries).
    • Do 2–3 timed DS/algo problems at easy/medium focusing on arrays, strings, hash maps, basic graphs, and simple dynamic programming, framed as data tasks (log processing, aggregation, etc.).
  2. SQL bootstrapping (Meta/Netflix/Google aligned) — daily 45–60 minutes:
    • Complex joins, subqueries, CTEs, window functions, cumulative metrics, retention cohorts, funnel analysis.
    • Practice writing queries from product prompts like: “Compute daily watch-start rate and CTR by title, handling late events.” (Very Netflix-like.)
  3. Data-engineering fundamentals refresh
    • Data modeling: star/snowflake, slowly changing dimensions, event schemas, partitioning, clustering, primary keys/idempotency.
    • Pipelines: batch vs streaming; backfills; late data handling; fault tolerance; exactly-once semantics; monitoring and SLAs.
    • Distributed systems basics: partitions, replication, consistency vs availability, idempotent writes, schema evolution.
  4. Stories inventory
    • Write bullet lists for 8–10 major projects emphasizing: impact, scale, ambiguity, cross-team influence, and design tradeoffs.
    • Map each story to Meta “ownership,” Google leadership, Netflix culture (freedom & responsibility), OpenAI/Anthropic mission/ethics.

Weeks 3–4: SQL/coding deepening and system design

  1. SQL and coding (3–4 sessions/week)
    • Alternate days: one heavy SQL case study, one coding session.
    • SQL cases: full end-to-end product questions — metrics, experiment analysis, or canonical fact tables.
    • Coding: small ETL-style Python scripts (parsing logs, aggregating events, computing metrics) in 30–40 minutes; add one DS/algo problem each session.
  2. System-design drills (2 sessions/week) — one scenario per session:
    • Meta-style: “Design logging + data model + pipeline to track engagement metrics on a social feature.”
    • Netflix-style: “Design event logging and pipeline for the home UI for watch-start and CTR metrics, late/offline events, and PII constraints.”
    • Google-style: “Design a scalable, fault-tolerant pipeline for click logs supporting batch analytics and near-real-time dashboards.”
  3. Behavioral structure
    • Convert key stories into STAR/SAO format: ownership, conflict, cross-team influence, mentoring, failing and recovering, long-term vs short-term tradeoff.
    • Practice 2–3 answers/day out loud; record yourself.

Weeks 5–6: Advanced modeling, experimentation, and product sense

  1. Data modeling deep dives — for each company, design a realistic warehouse/lakehouse model for their core domain:
    • Meta: feeds, ads, notifications, user actions.
    • Netflix: titles, members, sessions, playback events, recommendation impressions.
    • Google: search queries/clicks, ads, or YouTube viewing.
    • OpenAI/Anthropic: model usage, tokens, experiments, safety events.
    Emphasize schema evolution, partitioning, PII handling, and data quality controls.
  2. Experimentation and metrics
    • North-star and guardrail metrics; metric hierarchies; interpreting experiment results, regressions, and tradeoffs.
    • Practice 2–3 product data cases per week: “Feature X launched; signups up 10%, retention down 2%. What do you do and how do you query to investigate?”
  3. Mock interviews (light)
    • At least 1 SQL + 1 system-design mock with peers or coaches.
    • After each, list recurring issues (over-explaining, missing edge cases, not asking clarifying questions, etc.).

Weeks 7–8: Company-specific sprints and starting processes

  1. Meta + Netflix + Google sprint
    • Meta: heavy SQL case practice with live coding; product analytics; ownership stories.
    • Netflix: pragmatic design — event logging, data products, recommendation-adjacent questions, cost/latency/correctness tradeoffs.
    • Google: open-ended problems, coding in plain editor; structured reasoning and tradeoffs.
  2. Start recruiter outreach — processes can span several weeks (often 3–5+ from application to offer). Use referrals; send a succinct profile and target blurb.
  3. Behavioral and “why us”
    • Draft crisp 2–3 minute answers for “Tell me about yourself,” “Why [company],” “Why now.”
    • Emphasize customer impact, large-scale systems, and alignment with their products and culture.

Weeks 9–10: OpenAI/Anthropic focus and deep architecture

  1. OpenAI-style prep
    • Take-home style: pipeline or data model, then live review of design and tradeoffs.
    • 1–2 “design a data platform for ML/LLM products” exercises: logging, feature stores, offline/online data, evaluation loops, safety signals.
  2. Anthropic-style prep
    • Coding with focus on correctness, clarity, and safety; general software and system design.
    • Prepare AI safety, ethics, risk, long-term tradeoffs; map experiences where you made conservative or ethical calls.
  3. Values and mission
    • Rehearse “Why OpenAI?” and “Why Anthropic?” tied to mission and safety; connect to your beliefs and experience.
    • Be ready to discuss ambiguous AI risk and high-impact decisions.

Weeks 11–12: Full loops and polish

  1. Simulated onsite days — at least two full “onsite days” with back-to-back sessions: 1 SQL, 1 coding, 1 system design, 1 product/metrics, 1 behavioral. Timebox 45–60 minutes each.
  2. Patch weak spots — if SQL is still a risk, prioritize it (often pass/fail). If system design is weaker, do 4–5 more focused designs with self-critique on tradeoffs and failure modes.
  3. Offer and negotiation prep — comp expectations by level and market; script for multiple offers and exploding deadlines.

4. Daily/weekly routine template

To stay consistent while working full time:

  • 3 evenings (1.5–2 hours each): 1 SQL-heavy session, 1 coding session, 1 system-design or product/metrics case.
  • 1 weekend block (3–4 hours): mock interview(s) plus behavioral/story refinement.
  • Short daily habits (15–20 minutes): one behavioral question out loud; review 3–5 flashcards (window functions, design patterns, metrics definitions).

Continue in the tracks

Use System Design for structured day-by-day architecture prep and Coding for the 90-day problem plan, SQL, Python, and patterns.