Meta Data Engineer Interview Guide

From hundreds of mock interviews, one pattern keeps showing up: candidates prep for Meta's data engineer loop like it's a software engineering interview with some SQL sprinkled in. It's not. SQL and data modeling carry far more weight here than coding algorithms, and the process includes dedicated rounds for both query writing and system design. If you walk in with a coding-first mindset, you're preparing for the wrong test.

Meta Data Engineer Role

You're building ETL workflows in Dataswarm, querying Hive and Presto over ORC-partitioned tables, and pulling source data from TAO, Meta's internal graph database. Success after year one means your tables have clean SLAs that downstream data scientists trust, and you've shipped at least one schema migration or new pipeline that a product team depends on daily.

A Typical Week

The breakdown that catches people off guard isn't the coding or the meetings. It's how much of your week goes to work that doesn't feel like "building." Infrastructure maintenance, on-call documentation, debugging flaky quality checks, cleaning up expired partitions. Pair that with the writing load (design docs, migration plans, downstream consumer audits) and you realize this role rewards operational discipline as much as technical creativity.

Projects & Impact Areas

Ads pipeline infrastructure is where most data engineers feel the pressure most acutely, because pipeline latency affects ad auction freshness for Meta's core revenue engine across Facebook, Instagram, and Messenger. Reality Labs sits at the other extreme: greenfield telemetry pipelines for Quest headsets in a division that's still finding product-market fit, which means more ambiguity and fewer established patterns. Cutting across both is the AI push, where data engineers build training data pipelines and feature stores that bridge raw warehouse tables with PyTorch-based recommendation and generative models.

Skills & What's Expected

The source data rates data architecture and pipeline skills at "expert" and software engineering at "high," which is expected. What surprises candidates is that business acumen and data visualization/communication are also rated "high." You'll partner with product DS teams to scope logging requirements for something like an Integrity classifier, then present the batch-vs-streaming tradeoff to non-technical stakeholders. ML knowledge, by contrast, is medium-weight: you won't train models, but you need to understand feature engineering well enough to build what the ML engineers actually consume.

Levels & Career Growth

E5 (senior) is the career level where Meta considers you fully autonomous, owning entire data domains and mentoring others. The jump to E6 is where careers stall, and it's not about writing better code. E6 requires demonstrable cross-team impact: you led a data platform initiative that changed how multiple pods operate, or you defined a schema standard that several teams adopted. Scope, not skill, is the bottleneck.

Work Culture

Meta requires three days in-office per week, with Tuesday through Thursday being the most common pattern at MPK. The culture is flat in a specific way: engineers push directly to pipeline configs without gatekeeping layers, and you'll ship production ETL your first month. Half-yearly performance reviews keep urgency constant, and bottom performers face managed-out cycles, so this isn't a place to coast.

Meta Data Engineer Compensation

The quarterly RSU payouts smooth out your cash flow nicely, but refresher grants are where comp gets interesting. Each year, Meta awards a new RSU grant layered on top of your remaining vest, sized by your performance rating. Strong performers can see their equity grow meaningfully year over year, while those rated lower receive noticeably smaller refreshers. This creates real divergence between engineers at the same level over time, which is worth factoring into your multi-year earnings expectations.

Competing offers are your strongest negotiation tool. According to Meta's own hiring framework, candidates have leverage to negotiate base salary, RSU grants, and sometimes signing bonuses. RSU grants tend to have the widest band of flexibility, so if you're choosing between pushing on base versus equity, equity is where you'll likely find more room. Bring a written offer that clearly breaks down the comp components, so the recruiter can map it against Meta's package and identify specific gaps to close.

Meta Data Engineer Interview Process

The process runs about six weeks from recruiter screen to offer. Inadequate SQL proficiency and weak data modeling are the two most frequently cited rejection reasons, which makes sense when you realize three of your seven rounds test exactly those skills. Candidates from what we've seen tend to over-index on algorithm prep and under-prepare for the SQL-heavy gauntlet, which is exactly backwards for this loop.

Poor communication is the silent killer that compounds everything else. Meta's interview tips explicitly emphasize articulating your thought process, assumptions, and tradeoffs clearly, not just arriving at the right answer. Structure your responses in clean, narrated steps so your reasoning is unmistakable, because a correct but muddled walkthrough of a schema design won't score the same as one where each decision is stated plainly.

Meta Data Engineer Interview Questions

1/* Daily distinct viewers per reel for the last 7 days.
2   Assumptions:
3   - Table: reels_events
4   - Columns: dt (VARCHAR 'YYYY-MM-DD'), event_ts (TIMESTAMP), user_id (BIGINT), reel_id (BIGINT), event_name (VARCHAR)
5   - View event is identified by event_name = 'reel_view'
6*/
7WITH params AS (
8  SELECT
9    current_date AS as_of_date,
10    date_add('day', -6, current_date) AS start_date
11),
12filtered AS (
13  SELECT
14    e.reel_id,
15    e.user_id,
16    CAST(e.event_ts AS DATE) AS event_date,
17    e.event_ts
18  FROM reels_events e
19  CROSS JOIN params p
20  WHERE e.event_name = 'reel_view'
21    AND e.user_id IS NOT NULL
22    -- Partition pruning
23    AND e.dt BETWEEN date_format(p.start_date, '%Y-%m-%d')
24               AND date_format(p.as_of_date, '%Y-%m-%d')
25    -- Correctness for late or mis-partitioned events
26    AND CAST(e.event_ts AS DATE) BETWEEN p.start_date AND p.as_of_date
27),
28dedup AS (
29  SELECT
30    reel_id,
31    user_id,
32    event_date,
33    ROW_NUMBER() OVER (
34      PARTITION BY reel_id, user_id, event_date
35      ORDER BY event_ts ASC
36    ) AS rn
37  FROM filtered
38)
39SELECT
40  event_date AS ds,
41  reel_id,
42  COUNT(*) AS daily_distinct_viewers
43FROM dedup
44WHERE rn = 1
45GROUP BY 1, 2
46ORDER BY 1, 2;
47

1from __future__ import annotations
2
3from datetime import datetime
4from typing import Any, Dict, List, Tuple
5
6
7def dedupe_events_keep_earliest(events: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
8    """Deduplicate by (user_id, event_id), keeping the earliest ts, stable output order.
9
10    If multiple duplicates exist, only the earliest-ts record is kept.
11    Output order matches the original order of the kept records as they appeared.
12
13    events: [{"user_id": ..., "event_id": ..., "ts": "2026-02-24T12:34:56Z", ...}, ...]
14    """
15
16    def parse_ts(ts: str) -> datetime:
17        # Accept common ISO-8601 forms, including a trailing 'Z'.
18        # datetime.fromisoformat does not accept 'Z' in some versions.
19        if ts.endswith("Z"):
20            ts = ts[:-1] + "+00:00"
21        return datetime.fromisoformat(ts)
22
23    # Map (user_id, event_id) -> (best_timestamp, kept_index)
24    best: Dict[Tuple[Any, Any], Tuple[datetime, int]] = {}
25
26    # Keep a mutable list so we can "remove" earlier kept items if we find an earlier ts later.
27    kept: List[Dict[str, Any] | None] = []
28
29    for e in events:
30        key = (e.get("user_id"), e.get("event_id"))
31        ts = parse_ts(e["ts"])
32
33        if key not in best:
34            best[key] = (ts, len(kept))
35            kept.append(e)
36            continue
37
38        best_ts, kept_idx = best[key]
39        if ts < best_ts:
40            # Replace: remove the old kept event, keep this earlier one.
41            kept[kept_idx] = None
42            best[key] = (ts, len(kept))
43            kept.append(e)
44        # Else, drop this event.
45
46    return [e for e in kept if e is not None]
47

1from __future__ import annotations
2
3from collections import defaultdict, deque
4from datetime import datetime, timedelta
5from typing import Any, Deque, Dict, List, Tuple
6
7
8def rolling_10m_counts_per_user(events: List[Dict[str, Any]]) -> List[int]:
9    """For each event, compute count of events by that user in the last 10 minutes (inclusive).
10
11    Assumptions:
12      - Input is globally sorted by ts (non-decreasing).
13      - Each event has keys: user_id, ts (ISO-8601).
14
15    Returns:
16      - counts[i] corresponds to events[i].
17
18    Time complexity is O(n) total because each event enters and leaves its user's deque once.
19    """
20
21    def parse_ts(ts: str) -> datetime:
22        if ts.endswith("Z"):
23            ts = ts[:-1] + "+00:00"
24        return datetime.fromisoformat(ts)
25
26    window = timedelta(minutes=10)
27
28    # user_id -> deque of timestamps in the current window
29    q: Dict[Any, Deque[datetime]] = defaultdict(deque)
30
31    out: List[int] = []
32
33    for e in events:
34        uid = e["user_id"]
35        t = parse_ts(e["ts"])
36
37        dq = q[uid]
38        dq.append(t)
39
40        # Pop strictly older than (t - window). Inclusive window means keep timestamps >= t-window.
41        cutoff = t - window
42        while dq and dq[0] < cutoff:
43            dq.popleft()
44
45        out.append(len(dq))
46
47    return out
48

Meta's weighting tells a specific story: the areas that matter most all revolve around how data moves through and gets shaped inside their Hive/Presto warehouse ecosystem, not whether you can invert a binary tree. What makes this loop unusually punishing is that a single scenario (say, modeling an ad impressions fact table) can test your schema grain choices, your ability to write the Presto query that backfills it, and your SLA recovery plan when upstream TAO data arrives late, all in one conversation. From what candidates report, the most common misallocation is grinding Python problems while barely practicing verbal schema design walkthroughs, which is the skill Meta's interviewers actually probe hardest.

Practice Meta-specific questions across all six areas at datainterview.com/questions.

How to Prepare for Meta Data Engineer Interviews

Meta generated $201B in revenue in 2025, up roughly 24% year over year. Where that money goes next is what matters for your prep. Zuckerberg's 2026 roadmap is explicitly AI-first, funneling capital into recommendation models, generative AI products, and the training infrastructure underneath them. Meanwhile, Reality Labs is separating its Quest VR platform from Worlds and shifting Worlds to mobile, creating greenfield data problems around telemetry, developer analytics, and cross-platform engagement. As a data engineer, you're building pipelines that serve both the revenue engine and these long-horizon bets simultaneously.

The "why Meta" answer that actually works ties your experience to a specific pipeline domain, not a vague love of scale. Talk about how ads data freshness directly constrains ranking model quality, or how Reality Labs needs to improve analytics to help VR developers understand their audience. Even better, reference how feature stores bridge warehouse data and PyTorch-based agentic systems. Interviewers want proof you've thought about where your pipelines end up, not just how they're built.

Try a Real Interview Question

1WITH params AS (
2  SELECT CAST(:as_of_date AS DATE) AS as_of_date
3),
4last_7_days AS (
5  SELECT d.dt
6  FROM date_dim d
7  CROSS JOIN params p
8  WHERE d.dt BETWEEN (p.as_of_date - INTERVAL '6' DAY) AND p.as_of_date
9),
10dataset_days AS (
11  SELECT ds.dataset_id, ds.sla_hours, d.dt
12  FROM datasets ds
13  CROSS JOIN last_7_days d
14),
15runs_in_window AS (
16  SELECT
17    pr.dataset_id,
18    CAST(pr.scheduled_at AS DATE) AS dt,
19    pr.completed_at,
20    pr.status,
21    ROW_NUMBER() OVER (
22      PARTITION BY pr.dataset_id, CAST(pr.scheduled_at AS DATE)
23      ORDER BY pr.completed_at DESC
24    ) AS rn
25  FROM pipeline_runs pr
26  JOIN params p
27    ON CAST(pr.scheduled_at AS DATE) BETWEEN (p.as_of_date - INTERVAL '6' DAY) AND p.as_of_date
28),
29latest_run_per_day AS (
30  SELECT dataset_id, dt, completed_at, status
31  FROM runs_in_window
32  WHERE rn = 1
33),
34scored AS (
35  SELECT
36    dd.dataset_id,
37    dd.dt,
38    CASE
39      WHEN lr.status = 'success'
40       AND lr.completed_at IS NOT NULL
41       AND EXTRACT(EPOCH FROM (lr.completed_at - CAST(dd.dt AS TIMESTAMP))) / 3600.0 <= dd.sla_hours
42      THEN 1 ELSE 0
43    END AS is_compliant
44  FROM dataset_days dd
45  LEFT JOIN latest_run_per_day lr
46    ON lr.dataset_id = dd.dataset_id
47   AND lr.dt = dd.dt
48)
49SELECT
50  dataset_id,
51  SUM(is_compliant) AS compliant_days,
52  COUNT(*) AS total_days,
53  1.0 * SUM(is_compliant) / COUNT(*) AS compliance_rate
54FROM scored
55GROUP BY dataset_id
56ORDER BY compliance_rate DESC, dataset_id;

This type of problem is representative because Meta's SQL rounds reward you for narrating your approach before writing anything. The interviewers care as much about how you decompose a multi-step problem as whether your final query runs clean. Practice at datainterview.com/coding to build that habit of thinking out loud while writing production-quality SQL.

Test Your Readiness

Practice data modeling questions out loud, not just on paper. datainterview.com/questions is built for exactly that kind of verbal-first prep.