Meta Data Engineer Interview — AI Native Full Stack Round

Platform: CoderPad (app.coderpad.io) with integrated AI Assist
Level: IC5/IC6 Data Engineer
Round Type: Technical Screen (single interviewer)
Duration: 60 minutes (no breaks between sections)

1. Interview Structure & Timing

The interview consists of 4 consecutive sections that build upon a single continuous business scenario. Each section flows directly into the next — your answers in earlier sections shape what you'll be asked later.

[!IMPORTANT]
All 4 sections share one unified business scenario. The interviewer progressively builds on your answers. What you write in Section 1 directly impacts what you're asked in Sections 2, 3, and 4.

2. How Each Section Works

Section 1: Business Case (~15 min)

CoderPad Mode: Plain Text

What happens:

• The interviewer pastes a business context paragraph into the left panel describing a product scenario
• Below the context, there's a specific question asking you to respond in writing
• You type your answer directly in the CoderPad text editor
• Follow-up questions build on your response

What the interviewer evaluates:

• Can you identify ambiguity in a business ask?
• Do you ask the right clarifying questions before jumping to solutions?
• Can you think from multiple stakeholder perspectives?
• Do you consider both product success and potential negative impacts?

Question format:

• Open-ended text prompts (not multiple choice)
• Write answers as if responding to a PM/stakeholder
• Typically 2 sub-parts: (1) clarifying questions, (2) translate business goals into technical terms

What's expected:

• 4-6 well-structured clarifying questions
• Show understanding of vanity metrics vs. actionable metrics
• Cover multiple angles: growth, engagement, retention, cannibalization, user satisfaction
• Think about what "success" means for different stakeholders

Section 2: Data Modeling (~15 min)

CoderPad Mode: Plain Text

What happens:

• The interviewer provides system constraints (data scale, retention policies, etc.)
• You design a data model (dimension + fact tables) in the text editor
• Follow-up questions test if your model can answer new business questions without schema changes
• Additional follow-ups ask you to extend the model for new use cases

What the interviewer evaluates:

• Star schema design (fact vs. dimension tables)
• Appropriate grain selection for fact tables
• Understanding of partitioning strategies for large-scale data
• Foreign key relationships and entity identification
• Whether your model is flexible enough to answer ad-hoc questions
• How you'd extend an existing model vs. redesigning it

Question format:

• Text-based: "Design your core data model that supports X"
• Constraints are given (e.g., data volume, retention window)
• Follow-ups: "How does your model answer Y?" and "Now extend it for Z"
• Typically 3 sub-parts building in complexity

What's expected:

• Clear table definitions with column names, types, and keys
• Explicit grain statement ("my fact table is at the X-level grain")
• Partitioning strategy (date-based is most common)
• Ability to answer "how would you query this?" for your own model
• Show how existing dimension tables enable new analyses via joins

Section 3: SQL (~15 min)

CoderPad Mode: PostgreSQL 12.4 (runnable)

What happens:

• CoderPad switches to SQL mode with a live PostgreSQL database
• A pre-written SQL query is pasted into the left panel (claimed to be "AI-generated")
• The right panel has tabs: Instructions, Program Output, Database (schema explorer), AI Assist
• You need to find errors in the query, then fix them
• After fixing syntax/logic errors, you run the query and find data quality issues in the output

This section has 2 sub-parts:

Part A: Find Errors in SQL (~8–10 min)

• You're given a query with intentional errors (at least 4)
• Errors span: syntax issues, wrong join types, redundant joins, fan-out problems, missing columns
• You can edit the query directly and run it against the live DB

Part B: Find Data Issues (~5–7 min)

• After fixing the query, you run it and inspect the output data
• You identify data quality problems (negative values, edge cases, etc.)
• AI explicitly cannot help with this part — you must spot issues yourself
• You update the query to handle the data issues

Database Schema:

• The right panel has a Database tab with a visual schema explorer
• Shows all tables with column names and data types
• Typically includes 2-3 dimension tables and 1 fact table

What the interviewer evaluates:

• Can you read and debug someone else's SQL?
• Do you understand join semantics (INNER vs LEFT vs fan-out)?
• Can you spot logical errors vs just syntax errors?
• Data quality intuition — spotting impossible values, selection bias, etc.
• Can you reason about query performance (redundant scans, unnecessary CTEs)?

What's expected:

• Find 4+ critical errors (not just cosmetic issues)
• Understand why a LEFT JOIN vs INNER JOIN matters for analytical accuracy
• Recognize when a join creates row duplication (fan-out)
• Spot data issues that wouldn't cause the query to fail but produce wrong results
• Fix the query and validate the output makes sense

Section 4: Coding — Python (~15 min)

CoderPad Mode: Python Project (multi-file)

What happens:

• CoderPad switches to Python Project mode with a file explorer
• A README.md file describes the coding problem with:
  • Input data format and sample data (hardcoded in a data file)
  • Known data quality issues you must handle
  • A function signature you need to implement
  • Expected output format (dictionary)
• Source files are organized: src/, main.py, interview_data.py
• You write your solution and can run it with the "Run Main" button
• The right panel shows Instructions and Program Output

What the interviewer evaluates:

• Can you handle messy, real-world data (not clean textbook data)?
• Do you account for edge cases the prompt explicitly warns about?
• Code organization and readability
• Can you explain your approach verbally while coding?

Problem format:

• You receive two data sources from different systems/teams
• The data has known issues explicitly stated (e.g., schema changes, missing records, orphaned data)
• You write a function that processes the data and returns a summary dictionary
• The output should directly answer the original business question from Section 1

What's expected:

• Parse raw string data into structured objects
• Handle invalid/malformed events gracefully (skip, don't crash)
• Handle orphaned records (data in one source but not the other)
• Compute aggregate metrics grouped by categories
• Return a clean, well-structured dictionary
• Be able to explain your code's logic verbally

3. AI Usage Policy

[!IMPORTANT]
This is a critical part of the Meta interview format. AI is allowed but evaluated differently than you'd expect.

What the interviewer said (verbatim from the recording):

• "You can use AI during most of the sections" — AI is enabled throughout
• "I'll be evaluating your judgment, reasoning, and critical decision-making" — not just correctness
• "AI output on its own is not an answer" — the process and judgment matter more
• "Your final answer is what you write on the left side of the CoderPad" — AI suggestions on the right don't count
• "If you include AI-generated text, I'll probably ask you to explain why you chose that" — you must defend AI output
• "I'll ask follow-ups to get understanding of any AI-generated content" — expect deep probing
• "You can explain your approach in your own words" — verbal explanation is key

AI Tool Available:

• CoderPad AI Assist panel (right side) with a model selector dropdown
• Multiple models available including GPT (various versions), Claude Sonnet, Claude Opus, and others
• Has an "Ask something..." prompt where you can type questions
• Can generate suggestions, KPIs, code, SQL, etc.
• You can switch between models freely during the interview

What AI CAN help with:

• Brainstorming clarifying questions
• Suggesting metrics/KPIs you might have missed
• Generating SQL query drafts
• Writing Python code scaffolding
• Getting suggestions for data model extensions

What AI CANNOT help with:

• Finding data issues in SQL output (interviewer explicitly states this)
• Replacing your own judgment and reasoning
• Being your sole answer without explanation

How the AI was actually used in this interview: The candidate used AI to:

• Get a list of suggested clarifying questions (then selected/modified the relevant ones)
• Get suggested KPIs organized by primary/secondary categories
• Get suggestions for data model extensions for the ML section
• Ask AI to add comments to their code after writing it

[!META-RULE]
Use AI as a starting point, not a final answer. The interviewer will probe whether YOU understand what the AI suggested. If you can't explain it in your own words, it hurts more than it helps.

4. CoderPad Platform Layout

Left Panel (your workspace):

• Where you write answers (text, SQL, or code)
• In SQL mode: executable query editor with "Run" button
• In Python mode: multi-file project with file explorer and "Run Main" button

Right Panel (tools):

Other features:

• Screen sharing is required (candidate shares their screen)
• The interviewer can see everything you type in real-time
• There's a "History" tab showing change history
• The interviewer can toggle between different CoderPad question pads

5. Interview Flow & Transitions

Intro (2 min)

• AI policy explained
• Screen sharing setup
• CoderPad orientation

Section 1: Business Case (15 min)

• Interviewer pastes context + question
• Candidate writes clarifying questions
• Can use AI for brainstorming
• Follow-up: translate to metrics/dimensions

Section 2: Data Modeling (15 min)

• Interviewer adds constraints below existing text
• Candidate designs tables in same text editor
• Follow-up questions test model flexibility
• Follow-up: extend for new use case

Section 3: SQL (15 min)

• CoderPad switches to SQL mode (new pad)
• Pre-written query with errors appears
• Part A: Find and fix errors
• Part B: Run query, find data issues

Section 4: Coding (15 min)

• CoderPad switches to Python Project mode (new pad)
• README with problem + data in interview_data.py
• Write function, handle messy data
• Run and verify output

Wrap-Up (1-2 min)

• "Any questions for me?"

6. Key Expectations Per Section

What separates a strong candidate:

7. Technical Requirements

SQL Knowledge Required

• CTEs (WITH ... AS)
• JOIN types (INNER, LEFT) and when each is appropriate
• Aggregation functions (AVG, COUNT, SUM, ROUND)
• GROUP BY semantics
• CASE WHEN for conditional logic
• Understanding of fan-out from many-to-many joins
• Data type casting and NULL handling

Python Knowledge Required

• String parsing (splitting CSV-like strings)
• Dictionary operations (defaultdict, nested dicts)
• Data containers (dataclasses or named tuples)
• Iteration and conditional filtering
• Edge case handling (try/except, validation)
• Function design returning structured output

Data Modeling Knowledge Required

• Star schema (fact + dimension tables)
• Grain definition
• Partitioning strategies (date-based)
• Entity-relationship design
• Foreign key relationships
• Incremental data loading concepts

8. Preparation Tips

[!TIP]
Based on what was observed in this actual interview:

1. Practice the full pipeline: business question → metrics → data model → SQL → code. Meta tests all of it in one sitting.

2. Get comfortable with CoderPad: The platform has specific UI quirks (SQL vs Python modes, AI panel, database explorer, instructions panel). Familiarity saves time.

3. Practice explaining AI output: In a mock, use ChatGPT to answer a question, then practice explaining the answer as if it were yours. The interviewer WILL probe.

4. Master star schema design: You need to go from "business question" → fact/dimension tables in under 10 minutes.

5. Practice SQL debugging: Finding 4+ errors in a 30-line query is a specific skill. Practice reading others' SQL critically.

6. Handle messy data: The coding section intentionally gives you data with known issues. Don't write clean-path-only code.

7. Time management: ~15 min per section. Don't over-invest in one section and rush through the rest.

8. Talk while you work: The interviewer is watching your screen. Narrate your thought process, especially during SQL debugging and coding.

Related Reading

• FANG+ Crash Course
• AI Engineer Interview Prep
• Meta AI Coding Round — How It Works