What level is Data Engineer at Apple?

Apple uses ICT3 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 Apple Data Engineer in New York make?

Apple Data Engineer in New York offers span $225K-$346K across 5 samples from 2025-2026, with a median of $332K, median base $220K and median annual equity $100K. Typical experience range: 7-10 years..

Does Apple actually hire data engineers in New York?

Yes, Apple 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 Apple 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 Apple interview data engineers differently than software engineers?

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

Interview Guide · 2026

Apple Data Engineer Interview in New York (ICT3)

Apple (ICT3) Data Engineer loop: Secretive by design, domain-focused teams, strong preference for depth over breadth. 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

$160K–$200K base • $230K–$320K total (ICT3)

Loop duration

3.8 hours onsite

Rounds

5 rounds

Location

New York, NY

Compensation

Apple Data Engineer in New York total comp

Across 5 samples

Offer-report aggregate, 2025-2026. Level mapped: L4. Typical experience: 7-10 years (median 7).

25th percentile

$225K

Median total comp

$332K

75th percentile

$346K

Median base salary

$220K

Median annual equity

$100K

Practice problems

Apple data engineer practice set

4 problems

Practice sets surfaced for Apple 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 Calendar Untangled

Given a list of 'YYYY-MM-DD' date strings, return them sorted chronologically ascending. Lexicographic comparison happens to be correct for this format.

Open in practice environment→

Modelingmedium~25 min

Employee Application Time Tracking

We need to track how much time employees spend in each application. HR wants daily summaries of time-per-employee-per-application, and wants to flag any employee spending more than 10 hours/day in a single application. Design the schema to capture this data.

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

Apple 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.

New York comp matches Apple's reference band without a cost-of-living adjustment. Loop structure in New York matches the global Apple process; what differs is team placement and the compensation range.

The loop

How the interview actually runs

01Recruiter screen

30 min

Apple is unusually secretive, you will likely not know exactly what the team builds until after onsite. The recruiter confirms level and general interest.

→Accept the secrecy, pressing for details signals you care more about the project than the fit
→Emphasize depth: one area you know extremely well beats five you know superficially
→Ask about team culture, not just product

02Technical phone screen

60 min

SQL and coding. Apple DEs cover iCloud analytics, hardware telemetry, payments, retail, services, very different stacks. The screen is calibrated to the team.

→Prepare for Apple-specific contexts: device telemetry, retail analytics, subscription lifecycle
→Show breadth but go deep when asked. Apple interviewers push on follow-ups
→Don't assume the interviewer uses AWS. Apple's internal stack is heavily custom

03Onsite: SQL

60 min

SQL deep-dive in the context of the team's domain. Expect 2-3 problems, often involving time-series aggregations, device grouping, or subscription state transitions.

→Practice state-transition SQL (active → paused → canceled)
→Apple loves LAG/LEAD for detecting state changes between rows
→Expect subtle edge cases in the data, missing rows, timezone issues, duplicate events

04Onsite: pipeline design

60 min

Design a pipeline in the team's domain. Apple is weighty on privacy: differential privacy, on-device aggregation, and minimal data retention often come up.

→Privacy-preserving design is a real criterion, know differential privacy basics
→Be ready to discuss on-device vs server-side tradeoffs
→Long-term reliability wins over clever architecture

05Onsite: behavioral + team fit

45 min

Apple weights the team-fit signal heavily. Hiring managers look for candidates who will operate in a team's specific culture without requiring change from the team.

→Stories about going deep on one thing (vs jumping between many)
→Emphasis on craftsmanship and getting details right
→Collaboration stories within a single team, not cross-functional theater

Level bar

What Apple 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.

Apple-specific emphasis

Apple's loop is characterized by: Secretive by design, domain-focused teams, strong preference for depth over breadth. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.

Behavioral

How Apple frames behavioral rounds

Craftsmanship

Apple's DNA. They want engineers who obsess about details and quality, not just shipping.

“Describe something you built where the last 20% of polish took longer than the first 80%.”

Privacy-by-default thinking

Apple's public brand. Even backend DEs are expected to think about privacy implications of data collection and retention.

“Tell me about a data decision you made with privacy implications.”

Focus

Apple rewards saying no to good ideas to keep working on great ones. Stories about narrowing scope land well.

“Describe a time you cut features or scope to deliver something better.”

Long-term thinking

Apple's data systems often last a decade. Stories about designing for longevity outweigh stories about speed.

“Describe a technical decision you made with a multi-year horizon.”

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, Apple weights this round heavily
·Read Apple'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+ Apple-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 Apple 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

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

FAQ

Common questions

What level is Data Engineer at Apple?: Apple uses ICT3 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 Apple Data Engineer in New York make?: Apple Data Engineer in New York offers span $225K-$346K across 5 samples from 2025-2026, with a median of $332K, median base $220K and median annual equity $100K. Typical experience range: 7-10 years..
Does Apple actually hire data engineers in New York?: Yes, Apple 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 Apple?: 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 Apple 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 Apple interview data engineers differently than software engineers?: The tracks diverge. DE at Apple 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