What level is Data Engineer at Microsoft?

Microsoft uses 61-62 to designate Data Engineers; this is an IC-track level focused on shipped production pipelines end-to-end and can debug them when they break.

How much does a Microsoft Data Engineer in New York make?

Microsoft Data Engineer in New York offers span $199K-$257K across 19 samples from 2021-2026, with a median of $225K, median base $171K and median annual equity $32K. Typical experience range: 4-10 years..

Does Microsoft actually hire data engineers in New York?

Yes, Microsoft maintains a New York office and hires Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.

How long should I prepare for the Microsoft Data Engineer interview?

6-8 weeks is the standard window for a working DE. Less than 4 weeks almost always means cutting the behavioral prep short.

Does Microsoft interview data engineers differently than software engineers?

The tracks diverge. DE at Microsoft weights SQL and pipeline-design rounds, and interviewers expect specific production data experience that SWE loops don't probe.

Interview Guide · 2026

Microsoft Data Engineer Interview in New York (61-62)

Microsoft (61-62) Data Engineer loop: Azure-focused with a strong growth-mindset framing in behavioral rounds. Bar at this level: shipped production pipelines end-to-end and can debug them when they break. Typical 2-5 years of data engineering experience. The New York, NY office has its own hiring cadence; the page below adjusts comp bands accordingly.

Compensation

$145K–$175K base • $200K–$270K total (61-62)

Loop duration

3 hours onsite

Rounds

4 rounds

Location

New York, NY

Compensation

Microsoft Data Engineer in New York total comp

Across 19 samples

Offer-report aggregate, 2021-2026. Level mapped: L4. Typical experience: 4-10 years (median 7).

25th percentile

$199K

Median total comp

$225K

75th percentile

$257K

Median base salary

$171K

Median annual equity

$32K

Median total comp by year

2024

$207K n=4

2025

$247K n=7

2026

$218K n=6

Practice problems

Microsoft data engineer practice set

4 problems

Practice sets surfaced for Microsoft data engineer candidates by the same model that reads their job postings. Each card opens a working coding environment.

SQLeasy~5 min

Clean Latency Cast

During a data quality investigation, you found that the latency column in service health records contains some non-numeric strings. Return all records with latency converted to an integer, excluding any rows where the conversion would fail. Return all available fields.

Open in practice environment→

Pythonmedium~10 min

The Inverted Triangle

Given positive integer n, return a list of n strings. Row 0 has n asterisks, row 1 has n-1, ..., row n-1 has 1 asterisk.

Open in practice environment→

Modelingeasy~25 min

A Number for the Seller

A seller on our marketplace wants to see their total products listed and total revenue earned per day. Design the data model that supports this view, and choose between a dimensional model or an ER model. Justify your choice. Then write the SQL to produce the report.

Open in practice environment→

Architecturehard~25 min

Pharma Data Ingestion Pipeline with Governance

We're a pharmaceutical company ingesting data from clinical trial systems, commercial sales databases, and patient support program feeds. The data governance team has mandated that every dataset entering the warehouse must have a documented data quality check, a lineage trace, and an access control policy before it goes live. Design the ingestion pipeline and governance framework.

Open in practice environment→

Try itDaily signup-to-purchase funnel

Count signups and first-time purchases per day. Product-company favorite.

funnel.sql

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WITH first_purchase AS (
  SELECT user_id, MIN(event_date) AS first_purchase_date
  FROM events
  WHERE event_type = 'purchase'
  GROUP BY user_id
)
SELECT
  e.event_date AS day,
  COUNT(*) FILTER (WHERE e.event_type = 'signup') AS signups,
  COUNT(*) FILTER (WHERE e.event_type = 'purchase'
    AND e.event_date = fp.first_purchase_date) AS first_purchases
FROM events e
LEFT JOIN first_purchase fp ON e.user_id = fp.user_id
GROUP BY e.event_date
ORDER BY e.event_date;
WITH first_purchase AS (
  SELECT user_id, MIN(event_date) AS first_purchase_date
  FROM events
  WHERE event_type = 'purchase'
  GROUP BY user_id
)
SELECT
  e.event_date AS day,
  COUNT(*) FILTER (WHERE e.event_type = 'signup') AS signups,
  COUNT(*) FILTER (WHERE e.event_type = 'purchase'
    AND e.event_date = fp.first_purchase_date) AS first_purchases
FROM events e
LEFT JOIN first_purchase fp ON e.user_id = fp.user_id
GROUP BY e.event_date
ORDER BY e.event_date;

Click Run to execute. Edit the code above to experiment.

New York, NY

Microsoft in New York

Finance-adjacent DE work is common; fintech and trading firms compete with Big Tech on comp. Required comp range disclosures in NY job postings.

Offers in New York use the same reference compensation band; no local adjustment applies. Loop structure in New York matches the global Microsoft process; what differs is team placement and the compensation range.

The loop

How the interview actually runs

01Recruiter screen

30 min

Standard call, background, motivations, level calibration. Microsoft is friendlier than FAANG peers in screen tone.

→Mention Azure/Synapse/Fabric experience if you have it
→Be specific about the product area interest: Azure Data, Power BI, Dynamics, Xbox, Office
→Ask about growth trajectory. Microsoft has strong internal mobility

02Technical screen

60 min

SQL + coding. Microsoft loves T-SQL syntax specifically. Problems involve stored procedures, CTEs, window functions, and data validation logic.

→Know T-SQL specifics: MERGE, APPLY, CROSS APPLY, OUTPUT clause
→Expect problems on event stores, telemetry, or Azure service usage
→Show familiarity with Azure Data Factory / Synapse if the team uses them

03Onsite: Data system design

60 min

Design a data pipeline or analytics system, usually with Azure services as the default stack. Microsoft interviewers are pragmatic and expect cost awareness.

→Default to Azure services: ADF for orchestration, Synapse for warehouse, ADLS for lake
→Cost questions land. Microsoft cares about Azure consumption
→Cover failure modes and retry/backoff explicitly

04Growth mindset / as-appropriate

60 min

Behavioral round heavily themed around growth mindset: learning from failure, seeking feedback, adapting. Microsoft interviewers are trained on this framework explicitly.

→Have 2+ stories about changing your mind based on new information
→Growth-mindset language: 'I learned that I had been assuming X'
→Feedback stories: what critical feedback did you get, how did you act on it

Level bar

What Microsoft expects at Data Engineer

Pipeline ownership

Mid-level DEs own pipelines end-to-end. Interviewers expect stories about designing, deploying, and maintaining a data pipeline that has been in production for 6+ months.

SQL + Python or Spark fluency

SQL is the floor. Most teams also expect fluency in either Python for data manipulation (pandas, airflow DAGs) or Spark for larger-scale processing.

On-call debugging

You should have concrete stories about production incidents: what alert fired, how you diagnosed, what you fixed, and what post-mortem action you owned.

Microsoft-specific emphasis

Microsoft's loop is characterized by: Azure-focused with a strong growth-mindset framing in behavioral rounds. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.

Behavioral

How Microsoft frames behavioral rounds

Growth mindset

Satya Nadella's cultural north star. Microsoft interviewers explicitly score on this dimension.

“Tell me about a time you failed at something significant and what you did next.”

Customer obsession

Microsoft has shifted toward customer-facing thinking even for backend roles. DEs should think about the analyst or developer consuming their data.

“How have you made a downstream user's job easier through your data work?”

Respectful disagreement

Microsoft's culture rewards strong opinions delivered with humility. Aggressive-genius stories land poorly.

“Describe a technical disagreement and how you handled it.”

Cross-organization collaboration

Microsoft is federated. Azure, Office, Xbox, Dynamics all have different cultures. Working across them requires diplomacy.

“Tell me about collaborating with a partner team that had different goals.”

Prep timeline

Week-by-week preparation plan

8-10 weeks out

Foundations and gap analysis

·Do 10 medium SQL problems. Note which patterns feel slow
·Write out 2-3 behavioral stories per value, Microsoft weights this round heavily
·Read Microsoft's public engineering blog for recent architecture patterns
·Review your prior production work, pick 3-5 projects you can discuss in depth

6 weeks out

SQL and coding fluency

·Practice window functions until DENSE_RANK, ROW_NUMBER, LAG, LEAD are reflex
·Do 20+ Microsoft-style problems in their domain
·Time yourself: 25 min per medium, 35 min per hard
·Record yourself narrating approach aloud, communication is graded

4 weeks out

Pipeline awareness and behavioral depth

·Review pipeline architecture basics: idempotency, partitioning, backfill
·Practice explaining a pipeline you've worked on end-to-end in 5 minutes
·Refine behavioral stories based on mock feedback
·Do 10 more SQL problems at medium difficulty

2 weeks out

Behavioral polish and mock loops

·Rehearse every story out loud. Cut to 2-3 minutes each
·Run 2 full mock loops with a mid-level DE or coach
·Identify your 3 weakest behavioral areas and draft additional stories
·Review recent Microsoft news or earnings call for fresh talking points

Week of

Taper and logistics

·No new content. Review your notes only
·Sleep. Mental energy matters more than one more practice problem
·Confirm logistics: laptop charged, shared-doc tool tested, snack and water nearby
·Remember: interviewers want to find reasons to hire you, not to reject you

Other guides you'll want

Microsoft Data Engineer (global guide)→Microsoft Junior Data Engineer in New York→Microsoft Senior Data Engineer in New York→Microsoft Staff Data Engineer in New York→Microsoft Principal Data Engineer in New York→Microsoft Data Engineer in Seattle→Microsoft Data Engineer in San Francisco Bay Area→Microsoft Data Engineer in Austin→Microsoft Data Engineer Salary→Data Engineer Interview Prep (hub)→

FAQ

Common questions

What level is Data Engineer at Microsoft?: Microsoft uses 61-62 to designate Data Engineers; this is an IC-track level focused on shipped production pipelines end-to-end and can debug them when they break.
How much does a Microsoft Data Engineer in New York make?: Microsoft Data Engineer in New York offers span $199K-$257K across 19 samples from 2021-2026, with a median of $225K, median base $171K and median annual equity $32K. Typical experience range: 4-10 years..
Does Microsoft actually hire data engineers in New York?: Yes, Microsoft maintains a New York office and hires Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.
How is the Data Engineer loop different from other levels at Microsoft?: Data Engineer loops run the same stages as other levels, but interviewers calibrate difficulty to shipped production pipelines end-to-end and can debug them when they break, especially around production pipeline ownership and on-call debugging.
How long should I prepare for the Microsoft Data Engineer interview?: 6-8 weeks is the standard window for a working DE. Less than 4 weeks almost always means cutting the behavioral prep short.
Does Microsoft interview data engineers differently than software engineers?: The tracks diverge. DE at Microsoft weights SQL and pipeline-design rounds, and interviewers expect specific production data experience that SWE loops don't probe.

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Published 2026-04-20Updated 2026-04-20