Morgan Stanley Data Engineer Interview Guide

Most candidates prep for Morgan Stanley's data engineer interviews like they're interviewing at a tech company that happens to be inside a bank. That's backwards. The people who stall out aren't weak coders. They're the ones who can't explain why a backfill script needs an audit trail, or why a missed SLA before market open means an immediate escalation.

Morgan Stanley Data Engineer Role

You're building and maintaining the data pipelines that feed Wealth Management analytics, risk reporting, and client onboarding workflows. Parametric, Morgan Stanley's custom indexing subsidiary, shows up repeatedly in job postings, and those roles center on Snowflake-based pipelines where data quality has direct downstream consequences for portfolio decisions. Success after year one means you own multiple production DAGs end to end and stakeholders across teams know your name because you unblocked their data requests.

A Typical Week

The thing that surprises candidates coming from tech companies is how much of your week goes to non-coding work that still feels urgent. Debugging a flaky data quality assertion on a Wednesday afternoon, writing runbooks on Friday so the next on-call engineer doesn't start from scratch, triaging Slack questions from a quant team whose positions table suddenly has NULLs because a source system migrated over the weekend. These aren't interruptions to the "real work." At a bank, they are the real work.

Projects & Impact Areas

Parametric's Snowflake pipelines sit at one end of the spectrum, where you're ingesting client KYC data through PII masking transformations before it lands in an analytics layer. On the other end, teams are evaluating Apache Iceberg as a table format replacement for Hive, running proof-of-concept migrations with production position datasets to test schema evolution and time-travel queries. In between, you'll find work like migrating legacy PySpark batch jobs from on-prem Cloudera to Databricks, a pattern that shows up in code reviews and sprint planning across multiple teams.

Skills & What's Expected

The skill radar shows medium scores across the board, which honestly reflects how broad this role is. SQL and Python are non-negotiable for every posting, but the real filter in interviews (based on candidate reports) is whether you can design a pipeline with audit logging and PII handling baked in from day one. Knowing how to write a clean Airflow DAG matters less than knowing why that DAG needs idempotent backfill logic and compliance-grade logging. Candidates who can articulate those tradeoffs in a financial context separate themselves from people who've only built pipelines where the worst failure mode is a stale dashboard.

Levels & Career Growth

Job postings surface at Associate, VP, and Principal levels. The gap between Associate and VP isn't just technical depth. VPs drive cross-team alignment, own architecture decisions, and represent the data platform in conversations with quants or portfolio managers who care about their AUM tracking table, not your partitioning strategy. Parametric roles tend to offer a slightly more autonomous trajectory within the larger org, which can accelerate growth if you prefer ownership over hierarchy.

Work Culture

The culture notes in Morgan Stanley's own materials say at least three days per week in-office, with most data engineering teams defaulting to Tuesday through Thursday at the Times Square headquarters. Expect thorough code reviews, change management gates, and documentation overhead that'll feel heavy if you're coming from a startup. The upside is real: your production systems actually need to work before markets open, and the engineering culture treats that reliability as a point of pride rather than a burden.

Morgan Stanley Data Engineer Compensation

Reliable public comp data for Morgan Stanley data engineering roles is thin. From what candidates report, a meaningful chunk of total compensation comes through annual bonuses, which at most banks are discretionary rather than contractually guaranteed. That uncertainty is the biggest mental adjustment for anyone coming from an RSU-heavy tech offer.

If you're negotiating, don't burn all your energy on base salary. Banks tend to have less flexibility there compared to sign-on bonuses or first-year bonus guarantees, both of which give hiring managers more room to work with. Come prepared to frame any competing tech offer as a single total-comp number so the conversation stays apples-to-apples.

Morgan Stanley Data Engineer Interview Process

From what candidates report, the timeline from first recruiter call to offer tends to run longer than at most tech companies. Background and compliance checks at financial institutions can add friction you won't see at a startup. If you're juggling a competing deadline, flag it to your recruiter as early as possible so they can work with the process rather than against it.

Underestimating the behavioral round is, from candidate accounts, the most common regret. Morgan Stanley's Wealth Management division manages roughly $6 trillion in client assets, and pipelines feeding advisor-facing products carry regulatory weight. Interviewers want concrete evidence you've operated in environments where a bad data push has consequences beyond a rollback, think SLA misses that trigger compliance reviews or schema changes that break downstream audit trails. Come prepared with specific stories about production incidents you owned, not just systems you built.

Morgan Stanley Data Engineer Interview Questions

Data Pipelines & Engineering

Expect questions that force you to design reliable batch/streaming flows for training and online features (e.g., Kafka/Flink + Airflow/Dagster). You’ll be evaluated on backfills, late data, idempotency, SLAs, lineage, and operational failure modes.

What is the difference between a batch pipeline and a streaming pipeline, and when would you choose each?

EasyFundamentals

Sample Answer

Batch pipelines process data in scheduled chunks (e.g., hourly, daily ETL jobs). Streaming pipelines process data continuously as it arrives (e.g., Kafka + Flink). Choose batch when: latency tolerance is hours or days (daily reports, model retraining), data volumes are large but infrequent, and simplicity matters. Choose streaming when you need real-time or near-real-time results (fraud detection, live dashboards, recommendation updates). Most companies use both: streaming for time-sensitive operations and batch for heavy analytical workloads, model training, and historical backfills.

You ingest Kafka events for booking state changes (created, confirmed, canceled) into a Hive table, then daily compute confirmed_nights per listing for search ranking. How do you make the Spark job idempotent under retries and late-arriving cancels without double counting?

AirbnbMediumIdempotency and Late Data

Sample Answer

Most candidates default to append-only aggregations with a checkpoint, but that fails here because duplicates and late cancels mutate history and you will overcount. You need a stable event key (booking_id plus event_version or event_timestamp plus source offset) and a dedupe rule, then compute state using last-write-wins or a state machine per booking. Write results with upserts (partition overwrite, MERGE, or Hudi/Iceberg style) keyed by booking_id and listing_id, and drive the aggregation off the canonical booking state, not raw events. Add watermarking and a correction window so late cancels trigger targeted recompute instead of full backfills.

You need a pipeline that produces a near real-time host payout ledger: streaming updates every minute, but also a daily audited snapshot that exactly matches finance when late adjustments arrive up to 30 days. Design the batch plus streaming architecture, including how you handle schema evolution and backfills without breaking downstream tables.

AirbnbHardLambda Architecture and Backfills

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System Design

Most candidates underestimate how much your design must balance latency, consistency, and cost at top tech companies scale. You’ll be evaluated on clear component boundaries, failure modes, and how you’d monitor and evolve the system over time.

Design a dataset registry for LLM training and evaluation that lets you reproduce any run months later, including the exact prompt template, filtering rules, and source snapshots. What metadata and storage layout do you require, and which failure modes does it prevent?

AnthropicMediumDataset Versioning and Lineage

Sample Answer

Use an immutable, content-addressed dataset registry that writes every dataset as a manifest of exact source pointers, transforms, and hashes, plus a separate human-readable release record. Store raw sources append-only, store derived datasets as partitioned files keyed by dataset_id and version, and capture code commit SHA, config, and schema in the manifest so reruns cannot drift. This prevents silent data changes, schema drift, and accidental reuse of a similarly named dataset, which is where most people fail.

A company wants a unified fact table for Marketplace Orders (bookings, cancellations, refunds, chargebacks) that supports finance reporting and ML features, while source systems emit out-of-order updates and occasional duplicates. Design the data model and pipeline, including how you handle upserts, immutable history, backfills, and data quality gates at petabyte scale.

AirbnbHardWarehouse Modeling and Incremental Processing

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SQL & Data Manipulation

Your SQL will get stress-tested on joins, window functions, deduping, and incremental logic that mirrors real ETL/ELT work. Common pitfalls include incorrect grain, accidental fan-outs, and filtering at the wrong stage.

Airflow runs a daily ETL that builds fact_host_daily(host_id, ds, active_listings, booked_nights). Source tables are listings(listing_id, host_id, created_at, deactivated_at) and bookings(booking_id, listing_id, check_in, check_out, status, created_at, updated_at). Write an incremental SQL for ds = :run_date that counts active_listings at end of day and booked_nights for stays overlapping ds, handling late-arriving booking updates by using updated_at.

AirbnbMediumIncremental ETL and Late Arriving Data

Sample Answer

Walk through the logic step by step as if thinking out loud. You start by defining the day window, ds start and ds end. Next, active_listings is a snapshot metric, so you count listings where created_at is before ds end, and deactivated_at is null or after ds end. Then booked_nights is an overlap metric, so you compute the intersection of [check_in, check_out) with [ds, ds+1), but only for non-canceled bookings. Finally, for incrementality you only scan bookings that could affect ds, either the stay overlaps ds or the record was updated recently, and you upsert the single ds partition for each host.

SQLCopy

1WITH params AS (
2  SELECT
3    CAST(:run_date AS DATE) AS ds,
4    CAST(:run_date AS TIMESTAMP) AS ds_start_ts,
5    CAST(:run_date AS TIMESTAMP) + INTERVAL '1' DAY AS ds_end_ts
6),
7active_listings_by_host AS (
8  SELECT
9    l.host_id,
10    p.ds,
11    COUNT(*) AS active_listings
12  FROM listings l
13  CROSS JOIN params p
14  WHERE l.created_at < p.ds_end_ts
15    AND (l.deactivated_at IS NULL OR l.deactivated_at >= p.ds_end_ts)
16  GROUP BY l.host_id, p.ds
17),
18-- Limit booking scan for incremental run.
19-- Assumption: you run daily and keep a small lookback for late updates.
20-- This reduces IO while still catching updates that change ds attribution.
21bookings_candidates AS (
22  SELECT
23    b.booking_id,
24    b.listing_id,
25    b.check_in,
26    b.check_out,
27    b.status,
28    b.updated_at
29  FROM bookings b
30  CROSS JOIN params p
31  WHERE b.updated_at >= p.ds_start_ts - INTERVAL '7' DAY
32    AND b.updated_at < p.ds_end_ts + INTERVAL '1' DAY
33),
34booked_nights_by_host AS (
35  SELECT
36    l.host_id,
37    p.ds,
38    SUM(
39      CASE
40        WHEN bc.status = 'canceled' THEN 0
41        -- Compute overlap nights between [check_in, check_out) and [ds, ds+1)
42        ELSE GREATEST(
43          0,
44          DATE_DIFF(
45            'day',
46            GREATEST(CAST(bc.check_in AS DATE), p.ds),
47            LEAST(CAST(bc.check_out AS DATE), p.ds + INTERVAL '1' DAY)
48          )
49        )
50      END
51    ) AS booked_nights
52  FROM bookings_candidates bc
53  JOIN listings l
54    ON l.listing_id = bc.listing_id
55  CROSS JOIN params p
56  WHERE CAST(bc.check_in AS DATE) < p.ds + INTERVAL '1' DAY
57    AND CAST(bc.check_out AS DATE) > p.ds
58  GROUP BY l.host_id, p.ds
59),
60final AS (
61  SELECT
62    COALESCE(al.host_id, bn.host_id) AS host_id,
63    (SELECT ds FROM params) AS ds,
64    COALESCE(al.active_listings, 0) AS active_listings,
65    COALESCE(bn.booked_nights, 0) AS booked_nights
66  FROM active_listings_by_host al
67  FULL OUTER JOIN booked_nights_by_host bn
68    ON bn.host_id = al.host_id
69   AND bn.ds = al.ds
70)
71-- In production this would be an upsert into the ds partition.
72SELECT *
73FROM final
74ORDER BY host_id;

Event stream table listing_price_events(listing_id, event_time, ingest_time, price_usd) can contain duplicates and out-of-order arrivals. Write SQL to build a daily snapshot table listing_price_daily(listing_id, ds, price_usd, event_time) for ds = :run_date using the latest event_time within the day, breaking ties by latest ingest_time, and ensuring exactly one row per listing per ds.

AirbnbHardWindow Functions and Deduping

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Data Warehouse

A the company client wants one the company account shared by 15 business units, each with its own analysts, plus a central the company X delivery team that runs dbt and Airflow. Design the warehouse layer and access model (schemas, roles, row level security, data products) so units cannot see each other’s data but can consume shared conformed dimensions.

Boston Consulting Group (BCG)MediumMulti-tenant warehouse architecture and access control

Sample Answer

Most candidates default to separate databases per business unit, but that fails here because conformed dimensions and shared transformation code become duplicated and drift fast. You want a shared curated layer for conformed entities (customer, product, calendar) owned by a platform team, plus per unit marts or data products with strict role based access control. Use the company roles with least privilege, database roles, and row access policies (and masking policies) keyed on tenant identifiers where physical separation is not feasible. Put ownership, SLAs, and contract tests on the shared layer so every unit trusts the same definitions.

A Redshift cluster powers an operations dashboard where 150 concurrent users run the same 3 queries, one query scans fact_clickstream (10 TB) joined to dim_sku and dim_marketplace and groups by day and marketplace, but it spikes to 40 minutes at peak. What concrete Redshift table design changes (DISTKEY, SORTKEY, compression, materialized views) and workload controls would you apply, and how do you validate each change with evidence?

AmazonHardRedshift Physical Design and Concurrency

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Data Modeling

Rather than raw SQL skill, you’re judged on how you structure facts, dimensions, and metrics so downstream analytics stays stable. Watch for prompts around SCD types, grain definition, and metric consistency across Sales/Analytics consumers.

A company has a daily snapshot table listing_snapshot(listing_id, ds, price, is_available, host_id, city_id) and an events table booking_event(booking_id, listing_id, created_at, check_in, check_out). Write SQL to compute booked nights and average snapshot price at booking time by city and ds, where snapshot ds is the booking created_at date.

AirbnbMediumSnapshot vs Event Join

Sample Answer

Start with what the interviewer is really testing: "This question is checking whether you can align event time to snapshot time without creating fanout joins or time leakage." You join booking_event to listing_snapshot on listing_id plus the derived snapshot date, then aggregate nights as $\text{datediff}(\text{check\_out}, \text{check\_in})$. You also group by snapshot ds and city_id, and you keep the join predicates tight so each booking hits at most one snapshot row.

SQLCopy

1SELECT
2  ls.ds,
3  ls.city_id,
4  SUM(DATE_DIFF('day', be.check_in, be.check_out)) AS booked_nights,
5  AVG(ls.price) AS avg_snapshot_price_at_booking
6FROM booking_event be
7JOIN listing_snapshot ls
8  ON ls.listing_id = be.listing_id
9 AND ls.ds = DATE(be.created_at)
10GROUP BY 1, 2;

You are designing a star schema for host earnings and need to support two use cases: monthly payouts reporting and real-time fraud monitoring on payout anomalies. How do you model payout facts and host and listing dimensions, including slowly changing attributes like host country and payout method, so both use cases stay correct?

AirbnbHardSCD and Multi-Use Fact Modeling

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Coding & Algorithms

Your ability to reason about constraints and produce correct, readable Python under time pressure is a major differentiator. You’ll need solid data-structure choices, edge-case handling, and complexity awareness rather than exotic CS theory.

Given a stream of (asin, customer_id, ts) clicks for an detail page, compute the top K ASINs by unique customer count within the last 24 hours for a given query time ts_now. Input can be unsorted, and you must handle duplicates and out-of-window events correctly.

AmazonMediumSliding Window Top-K

Sample Answer

Get this wrong in production and your top ASIN dashboard flaps, because late events and duplicates inflate counts and reorder the top K every refresh. The right call is to filter by the $24$ hour window relative to ts_now, dedupe by (asin, customer_id), then use a heap or partial sort to extract K efficiently.

PythonCopy

1from __future__ import annotations
2
3from datetime import datetime, timedelta
4from typing import Iterable, List, Tuple, Dict, Set
5import heapq
6
7
8def _parse_time(ts: str) -> datetime:
9    """Parse ISO-8601 timestamps, supporting a trailing 'Z'."""
10    if ts.endswith("Z"):
11        ts = ts[:-1] + "+00:00"
12    return datetime.fromisoformat(ts)
13
14
15def top_k_asins_unique_customers_last_24h(
16    events: Iterable[Tuple[str, str, str]],
17    ts_now: str,
18    k: int,
19) -> List[Tuple[str, int]]:
20    """Return top K (asin, unique_customer_count) in the last 24h window.
21
22    events: iterable of (asin, customer_id, ts) where ts is ISO-8601 string.
23    ts_now: window reference time (ISO-8601).
24    k: number of ASINs to return.
25
26    Ties are broken by ASIN lexicographic order (stable, deterministic output).
27    """
28    now = _parse_time(ts_now)
29    start = now - timedelta(hours=24)
30
31    # Deduplicate by (asin, customer_id) within the window.
32    # If events are huge, you would partition by asin or approximate, but here keep it exact.
33    seen_pairs: Set[Tuple[str, str]] = set()
34    customers_by_asin: Dict[str, Set[str]] = {}
35
36    for asin, customer_id, ts in events:
37        t = _parse_time(ts)
38        if t < start or t > now:
39            continue
40        pair = (asin, customer_id)
41        if pair in seen_pairs:
42            continue
43        seen_pairs.add(pair)
44        customers_by_asin.setdefault(asin, set()).add(customer_id)
45
46    # Build counts.
47    counts: List[Tuple[int, str]] = []
48    for asin, custs in customers_by_asin.items():
49        counts.append((len(custs), asin))
50
51    if k <= 0:
52        return []
53
54    # Get top K by count desc, then asin asc.
55    # heapq.nlargest uses the tuple ordering, so use (count, -) carefully.
56    top = heapq.nlargest(k, counts, key=lambda x: (x[0], -ord(x[1][0]) if x[1] else 0))
57
58    # The key above is not a correct general lexicographic tiebreak, so do it explicitly.
59    # Sort all candidates by (-count, asin) and slice K. This is acceptable for moderate cardinality.
60    top_sorted = sorted(((asin, cnt) for cnt, asin in counts), key=lambda p: (-p[1], p[0]))
61    return top_sorted[:k]
62
63
64if __name__ == "__main__":
65    data = [
66        ("B001", "C1", "2024-01-02T00:00:00Z"),
67        ("B001", "C1", "2024-01-02T00:01:00Z"),  # duplicate customer for same ASIN
68        ("B001", "C2", "2024-01-02T01:00:00Z"),
69        ("B002", "C3", "2024-01-01T02:00:00Z"),
70        ("B003", "C4", "2023-12-31T00:00:00Z"),  # out of window
71    ]
72    print(top_k_asins_unique_customers_last_24h(data, "2024-01-02T02:00:00Z", 2))
73

Given a list of nightly booking records {"listing_id": int, "guest_id": int, "checkin": int day, "checkout": int day} (checkout is exclusive), flag each listing_id that is overbooked, meaning at least one day has more than $k$ active stays, and return the earliest day where the maximum occupancy exceeds $k$.

AirbnbHardSweep Line Overlap Counting

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Data Engineering

You need to join a 5 TB Delta table of per-frame telemetry with a 50 GB Delta table of trip metadata on trip_id to produce a canonical fact table in the company. Would you rely on broadcast join or shuffle join, and what explicit configs or hints would you set to make it stable and cost efficient?

CruiseMediumSpark Joins and Partitioning

Sample Answer

You could force a broadcast join of the 50 GB table or run a standard shuffle join on trip_id. Broadcast wins only if the metadata table can reliably fit in executor memory across the cluster, otherwise you get OOM or repeated GC and retries. In most real clusters 50 GB is too big to broadcast safely, so shuffle join wins, then you make it stable by pre-partitioning or bucketing by trip_id where feasible, tuning shuffle partitions, and enabling AQE to coalesce partitions.

PythonCopy

1from pyspark.sql import functions as F
2
3# Inputs
4telemetry = spark.read.format("delta").table("raw.telemetry_frames")  # very large
5trips = spark.read.format("delta").table("dim.trip_metadata")          # large but smaller
6
7# Prefer shuffle join with AQE for stability
8spark.conf.set("spark.sql.adaptive.enabled", "true")
9spark.conf.set("spark.sql.adaptive.coalescePartitions.enabled", "true")
10
11# Right-size shuffle partitions, set via env or job config in practice
12spark.conf.set("spark.sql.shuffle.partitions", "4000")
13
14# Pre-filter early if possible to reduce shuffle
15telemetry_f = telemetry.where(F.col("event_date") >= F.date_sub(F.current_date(), 7))
16trips_f = trips.select("trip_id", "vehicle_id", "route_id", "start_ts", "end_ts")
17
18joined = (
19    telemetry_f
20    .join(trips_f.hint("shuffle_hash"), on="trip_id", how="inner")
21)
22
23# Write out with sane partitioning and file sizing
24(
25    joined
26    .repartition("event_date")
27    .write
28    .format("delta")
29    .mode("overwrite")
30    .option("overwriteSchema", "true")
31    .saveAsTable("canon.fact_telemetry_enriched")
32)

A company Support wants a governed semantic layer for "First Response Time" and "Resolution Time" across email and chat, and an LLM tool will answer questions using those metrics. How do you enforce metric definitions, data access, and quality guarantees so the LLM and Looker both return consistent numbers and do not leak restricted fields?

CoinbaseHardGovernance, Semantic Layer, and Access Controls

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Cloud Infrastructure

In practice, you’ll need to articulate why you’d pick Spark/Hive vs an MPP warehouse vs Cassandra for a specific workload. Interviewers look for pragmatic tradeoffs: throughput vs latency, partitioning/sharding choices, and operational constraints.

A the company warehouse for a client’s KPI dashboard has unpredictable concurrency, and monthly spend is spiking. What specific changes do you make to balance performance and cost, and what signals do you monitor to validate the change?

Boston Consulting Group (BCG)MediumCost and performance optimization

Sample Answer

The standard move is to right-size compute, enable auto-suspend and auto-resume, and separate workloads with different warehouses (ELT, BI, ad hoc). But here, concurrency matters because scaling up can be cheaper than scaling out if query runtime drops sharply, and scaling out can be required if queueing dominates. You should call out monitoring of queued time, warehouse load, query history, cache hit rates, and top cost drivers by user, role, and query pattern. You should also mention guardrails like resource monitors and workload isolation via roles and warehouse assignment.

You need near real-time order events (p95 under 5 seconds) for an Operations dashboard and also a durable replayable history for backfills, events are 20k per second at peak. How do you choose between Kinesis Data Streams plus Lambda versus Kinesis Firehose into S3 plus Glue, and what IAM, encryption, and monitoring controls do you put in place?

AmazonHardStreaming Ingestion Tradeoffs and Security

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The widget breaks down question areas and samples, so look at the shape of it rather than any single category. What catches people off guard is that regulatory context isn't a separate topic here; it compounds the difficulty of every other area. A SQL problem becomes harder when you're expected to reason about audit trail requirements mid-query, and a pipeline design question gets thornier when you need to account for data retention policies that stem from SEC and FINRA obligations Morgan Stanley operates under. The single biggest prep mistake? Studying each skill in isolation, then freezing when an interviewer asks you to connect, say, schema evolution choices to downstream compliance reporting needs in one continuous answer.

Practice with finance-aware data engineering questions at datainterview.com/questions.

How to Prepare for Morgan Stanley Data Engineer Interviews

Morgan Stanley is actively recruiting lead engineers to build out a tokenization platform and reported plans for a crypto wallet, which means new data ingestion patterns for blockchain-derived data are being designed right now. Meanwhile, Wealth Management has become a dominant revenue driver, and data engineers increasingly power advisor-facing products and custom indexing pipelines at Parametric, Morgan Stanley's custom indexing subsidiary. The firm also open-sourced CALM through FINOS, an architecture-as-code framework that standardizes how pipeline architectures get documented and validated across teams.

Name a specific initiative when you answer "why Morgan Stanley." You could talk about designing Snowflake pipelines at Parametric where data quality directly affects index construction for individual investors, or about the engineering culture signaled by CALM's open-source release. Vague answers about "wanting to work in finance" won't land when the interviewer builds tokenization infrastructure or ships architecture tooling to the open-source community.

Try a Real Interview Question

1WITH latest_event AS (
2  SELECT
3    e.transaction_id,
4    e.merchant_id,
5    e.event_ts,
6    DATE(e.event_ts) AS event_date,
7    e.status,
8    CAST(e.amount_usd AS NUMERIC) AS amount_usd,
9    ROW_NUMBER() OVER (
10      PARTITION BY e.transaction_id
11      ORDER BY e.event_ts DESC
12    ) AS rn
13  FROM payment_events e
14  WHERE DATE(e.event_ts) BETWEEN DATE '2026-01-10' AND DATE '2026-01-11'
15)
16SELECT
17  le.event_date,
18  le.merchant_id,
19  SUM(
20    CASE
21      WHEN le.status = 'COMPLETED' THEN le.amount_usd
22      WHEN le.status IN ('REFUNDED', 'CHARGEBACK') THEN -le.amount_usd
23      ELSE CAST(0 AS NUMERIC)
24    END
25  ) AS net_amount_usd
26FROM latest_event le
27WHERE le.rn = 1
28GROUP BY le.event_date, le.merchant_id
29ORDER BY le.event_date, le.merchant_id;

From what candidates report, Morgan Stanley's technical rounds reward your ability to reason about data quality and pipeline logic in context, not just produce a correct answer. Financial data scenarios (trade reconciliation, portfolio snapshots, slowly changing dimensions) show up often enough that practicing them specifically is worth your time at datainterview.com/coding.

Test Your Readiness

Rehearse questions covering Parametric's Snowflake stack, SLA reasoning, and schema evolution tradeoffs at datainterview.com/questions.