How much does a Amazon Data Engineer in Toronto make?

Looking at 343 sampled offers from 2019-2026, Amazon Data Engineer in Toronto total comp comes in at $95K median, ranging from $65K to $153K, median base $61K and median annual equity $23K. Typical experience range: 4-10 years..

Does Amazon actually hire data engineers in Toronto?

Yes, Amazon maintains a Toronto 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 Amazon Data Engineer interview?

Most working DEs find 6-8 weeks is about right. The technical prep scales with experience; the behavioral story bank is where candidates underestimate time.

Does Amazon interview data engineers differently than software engineers?

Yes, the DE track at Amazon emphasizes SQL depth, warehouse and pipeline design, and real production data experience (late data, backfills, quality checks), which generalist SWE loops don't test.

Interview Guide · 2026

Amazon Data Engineer Interview in Toronto (L5)

Hiring for Data Engineer at Amazon (L5) runs Leadership Principles woven into every round, with a Bar Raiser holding veto power. The hiring bar is shipped production pipelines end-to-end and can debug them when they break; the median candidate brings 2-5 years of DE experience. Below we dig into how this runs out of the Toronto office (Toronto, ON, Canada), with cost-of-living-adjusted compensation.

Compensation

$116K–$139K base • $173K–$218K total (L5)

Loop duration

3.8 hours onsite

Rounds

5 rounds

Location

Toronto, ON, Canada

Compensation

Amazon Data Engineer in Toronto total comp

Across 343 samples

Offer-report aggregate, 2019-2026. Level mapped: L5. Typical experience: 4-10 years (median 7).

25th percentile

$65K

Median total comp

$95K

75th percentile

$153K

Median base salary

$61K

Median annual equity

$23K

Median total comp by year

2020

$109K n=6

2021

$75K n=54

2022

$118K n=53

2023

$83K n=60

2024

$116K n=72

2025

$87K n=55

2026

$78K n=42

Practice problems

Amazon data engineer practice set

4 problems

Interview problems predicted for Amazon data engineers based on their actual job descriptions. Click any problem to work it in a live coding environment.

Pythonmedium~20 min

The Coin Vault

Given a target amount and a list of coin denominations, return the minimum coins needed using a greedy strategy: repeatedly take the largest coin that does not exceed the remaining amount. Return -1 if the greedy approach cannot make exact change.

Open in practice environment→

Modelingmedium~25 min

Livestream Analytics Schema

We're building the analytics backend for a livestream platform. Creators go live, viewers watch and interact through chat and gifts. We need to track everything for creator payouts, content recommendations, and engagement analytics. Can you design the data model?

Open in practice environment→

Architecturemedium~25 min

The Vendor Who Never Warns You

We receive monthly data files from an external vendor. The problem is that the file structure changes unpredictably; new columns appear, column names get renamed, and occasionally columns are dropped. The data feeds a set of analyst dashboards that must not break when the file format changes. Design the ingestion pipeline.

Open in practice environment→

Modelingmedium~35 min

Machine Process Event Log Schema

We collect structured logs from a fleet of machines. Each machine runs many processes, and we need to track when each process runs and how long it takes. Data scientists need to query metrics like average elapsed time per process and plot process timelines across machines. Design the data model, and describe how you'd load this data via an ETL.

Open in practice environment→

Try itTop 2 sellers by revenue in each marketplace

Classic DE round opener. Window function + partition. Edit to tweak the threshold.

top_sellers.sql

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
















WITH seller_totals AS (
  SELECT marketplace, seller_id, SUM(amount) AS revenue
  FROM seller_orders
  GROUP BY marketplace, seller_id
),
ranked AS (
  SELECT marketplace, seller_id, revenue,
    DENSE_RANK() OVER (
      PARTITION BY marketplace ORDER BY revenue DESC
    ) AS rk
  FROM seller_totals
)
SELECT marketplace, seller_id, revenue
FROM ranked
WHERE rk <= 2
ORDER BY marketplace, revenue DESC;
WITH seller_totals AS (
  SELECT marketplace, seller_id, SUM(amount) AS revenue
  FROM seller_orders
  GROUP BY marketplace, seller_id
),
ranked AS (
  SELECT marketplace, seller_id, revenue,
    DENSE_RANK() OVER (
      PARTITION BY marketplace ORDER BY revenue DESC
    ) AS rk
  FROM seller_totals
)
SELECT marketplace, seller_id, revenue
FROM ranked
WHERE rk <= 2
ORDER BY marketplace, revenue DESC;

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

Toronto, ON, Canada

Amazon in Toronto

Strong Canadian DE market. Comp is lower than US in CAD terms, more competitive in PPP terms. Work permits are straightforward for FAANG hires.

Toronto comp lands about 25% below the reference band in line with local market rates. International candidates interviewing for Toronto can expect visa sponsorship support from Amazon. The Toronto office's interview loop mirrors the global loop structure; team assignment and comp-band negotiation are the main local variables.

The loop

How the interview actually runs

01Recruiter screen

30 min

Logistics, team fit, and a light Leadership Principle question. Recruiters confirm seniority expectations before booking the loop. Misalignment here can downlevel the loop.

→Have a 60-second pitch that names 2-3 concrete data systems you've built
→Confirm the team. Amazon has hundreds of DE teams across AWS, Retail, Ads, Alexa, Prime Video, Pharmacy
→Ask about the comp band early to avoid end-of-loop misalignment

02Technical phone screen

60 min

One SQL problem, one Python or pipeline design problem, and 10-15 min of Leadership Principle questions. The SQL is harder than the Online Assessment, expect multi-step window functions or self-joins.

→Narrate approach before writing code. Amazon grades process, not just the final answer
→Name the LP before telling the story
→Prepare at least 2 stories per LP; follow-ups probe a third story on the same theme

03Onsite: SQL deep-dive

60 min

Two to three SQL problems with escalating difficulty, usually in Amazon contexts (seller performance, order fulfillment, inventory). Ends with 10 min of LP questions.

→Practice window functions across large partition cardinalities
→Be ready to rewrite correlated subqueries as joins and vice versa
→When asked about optimization, mention partition pruning and columnar storage

04Onsite: Bar Raiser

60 min

An interviewer from outside the hiring team with veto power. Heaviest on Leadership Principles, with one harder technical problem. Tests whether you raise Amazon's hiring bar.

→Bring a story where you were wrong and had to change course
→Quantify impact: cost saved, latency reduced, users affected
→If you don't know something, say so. Fabricating kills the loop faster than any technical gap

Level bar

What Amazon 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.

Amazon-specific emphasis

Amazon's loop is characterized by: Leadership Principles woven into every round, with a Bar Raiser holding veto power. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.

Behavioral

How Amazon frames behavioral rounds

Dive Deep

The most relevant LP for data engineers. Amazon wants DEs who trace anomalies through 3+ layers of the stack instead of patching symptoms.

“Tell me about a time you found a data quality issue that others had missed.”

Ownership

You built it, you own it, including on-call and long-term maintenance. Ownership extends beyond your explicit scope when dependencies break.

“Describe a situation where you went beyond your role to solve a problem.”

Bias for Action

Speed beats perfection. Amazon wants DEs who ship V1 in 2 weeks rather than a perfect solution in 3 months.

“Tell me about a time you made a decision without having all the information.”

Earn Trust

Trust comes from delivery and transparency. Bar Raisers test whether you can admit mistakes and communicate setbacks without spinning.

“Tell me about a time you delivered bad news to a stakeholder.”

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

Adjacent guides to check

Amazon Data Engineer (global guide)→Amazon Junior Data Engineer in Toronto→Amazon Senior Data Engineer in Toronto→Amazon Staff Data Engineer in Toronto→Amazon Principal Data Engineer in Toronto→Amazon Data Engineer in Seattle→Amazon Data Engineer in Washington DC→Amazon Data Engineer in New York→Amazon Data Engineer Salary→Data Engineer Interview Prep (hub)→

FAQ

Common questions

What level is Data Engineer at Amazon?: At Amazon, Data Engineer corresponds to the L5 level. The bar emphasizes shipped production pipelines end-to-end and can debug them when they break without people-management responsibilities.
How much does a Amazon Data Engineer in Toronto make?: Looking at 343 sampled offers from 2019-2026, Amazon Data Engineer in Toronto total comp comes in at $95K median, ranging from $65K to $153K, median base $61K and median annual equity $23K. Typical experience range: 4-10 years..
Does Amazon actually hire data engineers in Toronto?: Yes, Amazon maintains a Toronto 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 Amazon?: The format of the loop matches other levels; difficulty and evaluation shift to shipped production pipelines end-to-end and can debug them when they break, and questions at this level dig into production pipeline ownership and on-call debugging.
How long should I prepare for the Amazon Data Engineer interview?: Most working DEs find 6-8 weeks is about right. The technical prep scales with experience; the behavioral story bank is where candidates underestimate time.
Does Amazon interview data engineers differently than software engineers?: Yes, the DE track at Amazon emphasizes SQL depth, warehouse and pipeline design, and real production data experience (late data, backfills, quality checks), which generalist SWE loops don't test.

Continue your prep

Data Engineer Interview Prep, explore the full guide

50+ guides covering every round, company, role, and technology in the data engineer interview loop. Grounded in 2,817 verified interview reports across 929 companies, collected from real candidates.

Interview Rounds

By Company

By Role

By Technology

Decisions

Question Formats

Start your data engineer interview prep:The Complete Interview Prep Guide Practice Problems Mock Interviews SQL and Python Lessons Daily Challenges

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