Google Data Engineer Interview Guide

Google values algorithmic thinking more than other companies for DE roles. Expect two SQL/Python coding rounds, system design at Google scale, and a dedicated Googleyness round that evaluates collaboration and intellectual curiosity. This guide breaks down each round, common questions, BigQuery deep-dive, and what makes the Google bar different from the rest of FAANG.

Google

Technology · Mountain View, US

live data · June 11, 2026

DE total comp

$236K median

$184K–$260K · 13 verified reports

Hiring now

25 open DE roles

live from career pages

Team happiness

44 / 100 · Stressed

model score from employee signals

Layoff risk (30d)

Moderate

Employee sentiment

Glassdoor4.4 / 5
BlindMixed

Employees

5,001–50,000

5–6
Total interview rounds
2
Coding rounds (most among FAANG DE)
L3–L6
Level range for external hires
Hiring Committee
Who decides, not the interviewer

Google DE interview process: round by round

Six stages. The onsite has two coding rounds — more than Meta or Amazon. The Googleyness round is a Google-specific behavioral round.

  1. 01

    Recruiter screen

    Non-technical call. The recruiter reviews your background, explains the process, and checks role fit. Google has DE roles across Ads, Cloud (BigQuery team), YouTube, Search, and Waymo. Ask which team the role is for; the technical expectations vary significantly.

    • ▸Know Google's data ecosystem: BigQuery, Dataflow (Apache Beam), Pub/Sub, Cloud Composer (Airflow)
    • ▸Google recruiters ask about your data scale experience; quantify volumes and frequencies
    • ▸The recruiter may schedule a follow-up with the hiring manager before the technical rounds
  2. 02

    Technical phone screen

    One or two coding problems, usually in SQL or Python. Google values algorithmic thinking more than other companies for DE roles. You may get a SQL problem that requires efficient query design, or a Python problem involving data transformation. The interviewer shares a Google Doc for coding. No IDE, no autocomplete.

    • ▸Google DE phone screens can include algorithmic thinking (not full LeetCode, but more than Meta/Amazon)
    • ▸For SQL, expect multi-step queries with CTEs and window functions
    • ▸For Python, expect data manipulation: parsing, aggregation, or ETL logic
  3. 03

    Onsite: coding round 1

    SQL or Python coding. One or two problems at intermediate to advanced difficulty. If SQL, expect window functions, complex joins, and optimization discussion. If Python, expect data processing logic: reading structured data, transforming it, handling edge cases. Google interviewers assess code quality, not just correctness. Clean variable names, comments on tricky logic, and handling edge cases all matter.

    • ▸Write clean code even under pressure; Google evaluates readability
    • ▸If Python, use standard library plus pandas/numpy if relevant; avoid obscure libraries
    • ▸Discuss time and space complexity if asked; Google values efficiency awareness
  4. 04

    Onsite: coding round 2

    A second coding round, sometimes with a different language focus. If the first was SQL, this might be Python (or vice versa). The difficulty is comparable to the first round. Some teams include a data modeling component here: design tables for a Google product and write queries against them.

    • ▸If you struggle with one round, perform well in the next; Google evaluates holistically
    • ▸For data modeling questions, start with the use case, define the grain, then write the query
    • ▸Ask clarifying questions about edge cases; Google values thoroughness
  5. 05

    Onsite: system design

    Design a data pipeline or data platform. Examples: YouTube video analytics pipeline, Search query log processing, Ads attribution system, or a real-time feature store. Google interviewers expect you to reason about scale (Google scale is the largest in the industry), fault tolerance, exactly-once semantics, and data freshness.

    • ▸Start with requirements gathering: volume, latency SLA, consumers, freshness needs
    • ▸Reference Google technologies where appropriate: BigQuery, Dataflow, Bigtable, Pub/Sub
    • ▸Discuss tradeoffs explicitly: batch vs streaming, consistency vs availability, cost vs performance
  6. 06

    Onsite: Googleyness and leadership

    Google's behavioral round. They evaluate how you collaborate, handle ambiguity, navigate disagreements, and contribute to team culture. Unlike Amazon's LP-heavy approach, Google's behavioral round is one dedicated session. But it matters: a strong Googleyness signal can compensate for a borderline technical round.

    • ▸Prepare stories about cross-functional collaboration (working with ML engineers, analysts, product managers)
    • ▸Google values humility and intellectual curiosity; show that you learn from others
    • ▸Discuss a time you changed your mind based on new data; Google likes evidence-based decision making

Google DE loop vs other FAANG

What's different about the Google bar relative to other DE interviews you may have prepared for.

DimensionGoogleAmazonMetaMicrosoft
Coding rounds (onsite)2111
Algorithm depthMediumLightLightMedium
SQL depthMedium-HeavyHeavyHeavyMedium
System design rounds1111
Behavioral rounds1 (Googleyness)1 (LP-heavy)1 (sign + behavioral)1
Hiring decisionHiring CommitteeHiring Manager + Bar RaiserHiring ManagerHiring Manager

Real Google interview questions

Reported questions from this company's loops, tagged by domain, round, and level.

Data Modelingonsite data modeling· L52025

Design a schema that tracks customer address where the address changes

From igotanoffer Google DE interview page. Requires designing a slowly changing dimension schema to track historical address changes for customers. Expected approach: separate Address table with effective_from and effective_to dates, foreign key to Customers table. Interviewer probes on how to handle concurrent address updates, whether to use SCD Type 1 (overwrite) or Type 2 (add new row with date range), and how to query the current address efficiently.

SQLphone screen sql· L42024

Given a large table with datetime, employee, and customer_response columns, find the top 10 employees with the most phone numbers found in the customer_response column

Extract phone numbers from free-text customer_response using pattern matching (LIKE or regex), then aggregate by employee and rank to find top 10.

mixedphone screen sql· unknown2022

Google | Cloud Data Engineer | 9 yof

Company: Google\nRole: Cloud Data Engineer\nYOE: 9yrs\nRound : Phone Interview\nLocation: USA, Remote\n\nthere was two questions :\n\n1. Data Modelling and SQL - Asked to design a Data model for movie booking of a local movie theater, followed by a sql question from the data model you design. Question was : find the top movie for the current month based on the gross earning. \n\n2. Second was an algorithm as mentioned below. I used python to write it.\n\n```\nWrite a standalone program (not a shell script or query) to output the top accessed tables in a database from a system log file:\n\nA…

Prepare for the interview
01 / Open invite
02min.

Walk into Google knowing the SQL pattern they'll test.

a Google SQL query, the same shape a screen would give you.
The diff against expected. Where ties broke. What you missed.
sandbox
1SELECT user_id,
2 COUNT(*) AS sessions
3FROM events
4WHERE ts >= NOW() - INTERVAL '7 day'
5
Execute your solution0.4s avg.
GoogleInterview question
Solve a Google problem

Real Google interview questions

Reported questions from this company's loops, tagged by domain, round, and level.

Data Modelingonsite data modeling· L52025

Design a schema that tracks customer address where the address changes

From igotanoffer Google DE interview page. Requires designing a slowly changing dimension schema to track historical address changes for customers. Expected approach: separate Address table with effective_from and effective_to dates, foreign key to Customers table. Interviewer probes on how to handle concurrent address updates, whether to use SCD Type 1 (overwrite) or Type 2 (add new row with date range), and how to query the current address efficiently.

SQLphone screen sql· L42024

Given a large table with datetime, employee, and customer_response columns, find the top 10 employees with the most phone numbers found in the customer_response column

Extract phone numbers from free-text customer_response using pattern matching (LIKE or regex), then aggregate by employee and rank to find top 10.

mixedphone screen sql· unknown2022

Google | Cloud Data Engineer | 9 yof

Company: Google\nRole: Cloud Data Engineer\nYOE: 9yrs\nRound : Phone Interview\nLocation: USA, Remote\n\nthere was two questions :\n\n1. Data Modelling and SQL - Asked to design a Data model for movie booking of a local movie theater, followed by a sql question from the data model you design. Question was : find the top movie for the current month based on the gross earning. \n\n2. Second was an algorithm as mentioned below. I used python to write it.\n\n```\nWrite a standalone program (not a shell script or query) to output the top accessed tables in a database from a system log file:\n\nA…

Push Opens by Platform and Campaign

> The engagement team suspects iOS users are more responsive to push notifications than Android users. Show how many unique users opened a notification on each platform, sorted from most opens to fewest.

Real Google interview questions

Reported questions from this company's loops, tagged by domain, round, and level.

Data Modelingonsite data modeling· L52025

Design a schema that tracks customer address where the address changes

From igotanoffer Google DE interview page. Requires designing a slowly changing dimension schema to track historical address changes for customers. Expected approach: separate Address table with effective_from and effective_to dates, foreign key to Customers table. Interviewer probes on how to handle concurrent address updates, whether to use SCD Type 1 (overwrite) or Type 2 (add new row with date range), and how to query the current address efficiently.

SQLphone screen sql· L42024

Given a large table with datetime, employee, and customer_response columns, find the top 10 employees with the most phone numbers found in the customer_response column

Extract phone numbers from free-text customer_response using pattern matching (LIKE or regex), then aggregate by employee and rank to find top 10.

mixedphone screen sql· unknown2022

Google | Cloud Data Engineer | 9 yof

Company: Google\nRole: Cloud Data Engineer\nYOE: 9yrs\nRound : Phone Interview\nLocation: USA, Remote\n\nthere was two questions :\n\n1. Data Modelling and SQL - Asked to design a Data model for movie booking of a local movie theater, followed by a sql question from the data model you design. Question was : find the top movie for the current month based on the gross earning. \n\n2. Second was an algorithm as mentioned below. I used python to write it.\n\n```\nWrite a standalone program (not a shell script or query) to output the top accessed tables in a database from a system log file:\n\nA…

BigQuery features Google interviewers expect you to know

If you're interviewing on a BigQuery-heavy team (Cloud, Ads, Analytics), naming these features by name signals real platform experience.

FeatureWhat it doesWhy it matters in interviews
Partitioned tablesPARTITION BY DATE(col) reduces scan cost; queries filter partitions in WHERE.Cost-critical at Google scale; missing partition filter = scan whole table.
Clustered tablesCLUSTER BY orders rows physically; speeds up range and equality filters.Stacks with partitioning. Cluster by high-cardinality filter columns.
STRUCT and ARRAYNested types. STRUCT is a record; ARRAY is repeated values.BigQuery is built around these. Schema design questions test STRUCT modeling.
UNNESTFlattens an ARRAY into rows. Required for joining nested data.Most-tested BigQuery syntax in interviews.
APPROX_COUNT_DISTINCTHyperLogLog-based; trades 1-2% accuracy for ~100× speed.Mention this when asked about cardinality at scale.
Materialized viewsAuto-refreshing pre-aggregations. Cost vs freshness tradeoff.Reference for cost-optimization questions.
Information SchemaMetadata about tables, jobs, columns. Used for governance and monitoring.Comes up in data-quality system design questions.

Real Google interview questions

Reported questions from this company's loops, tagged by domain, round, and level.

Data Modelingonsite data modeling· L52025

Design a schema that tracks customer address where the address changes

From igotanoffer Google DE interview page. Requires designing a slowly changing dimension schema to track historical address changes for customers. Expected approach: separate Address table with effective_from and effective_to dates, foreign key to Customers table. Interviewer probes on how to handle concurrent address updates, whether to use SCD Type 1 (overwrite) or Type 2 (add new row with date range), and how to query the current address efficiently.

SQLphone screen sql· L42024

Given a large table with datetime, employee, and customer_response columns, find the top 10 employees with the most phone numbers found in the customer_response column

Extract phone numbers from free-text customer_response using pattern matching (LIKE or regex), then aggregate by employee and rank to find top 10.

mixedphone screen sql· unknown2022

Google | Cloud Data Engineer | 9 yof

Company: Google\nRole: Cloud Data Engineer\nYOE: 9yrs\nRound : Phone Interview\nLocation: USA, Remote\n\nthere was two questions :\n\n1. Data Modelling and SQL - Asked to design a Data model for movie booking of a local movie theater, followed by a sql question from the data model you design. Question was : find the top movie for the current month based on the gross earning. \n\n2. Second was an algorithm as mentioned below. I used python to write it.\n\n```\nWrite a standalone program (not a shell script or query) to output the top accessed tables in a database from a system log file:\n\nA…

Google-specific preparation tips

What makes a Google DE interview different from other companies. Each tip is independent.

Coding rounds

Google values algorithmic thinking for DEs

More than Meta or Amazon, Google expects DEs to think about efficiency. You may get a Python problem that requires understanding time complexity, not just producing correct output. Brush up on common data structures (heaps, hash maps, sorting) and their use in data processing.

Cloud / Ads / Analytics teams

Know BigQuery deeply

If you're interviewing at Google Cloud or an analytics-heavy team, BigQuery knowledge is expected. Know partitioned tables, clustered tables, nested and repeated fields (STRUCT and ARRAY), and how BigQuery's columnar storage affects query design. Cost optimization comes up explicitly.

Onsite design round

System design at Google scale

Google processes more data than almost any other company. Your system design answers should reference scale explicitly: petabytes of storage, billions of events per day, sub-second latency requirements. Know the difference between Google-scale problems and problems solvable with a single Redshift cluster.

Cross-round

Communication matters more than you think

Google interviewers explicitly evaluate communication. Can you explain your approach before coding? Walk through your design clearly? Respond to feedback and adjust? Practice explaining technical concepts to a non-expert audience. Strong interviewers report 'they explained it well' as a top positive signal.

Process

Hiring committees decide, not interviewers

At Google, interviewers submit feedback packets to a hiring committee. The committee makes the hire/no-hire decision. This means you need to perform consistently across all rounds — one strong round cannot compensate for multiple weak ones. But one weak round is not automatically disqualifying if the rest are strong.

Comp

Levels matter for compensation more than title

Google uses L3 (entry), L4 (mid), L5 (senior), L6 (staff). Most external hires come in at L4 or L5. L3 interviews focus on coding and basic SQL. L5 adds system design depth and cross-team impact. L6 requires org-level influence. Confirm the target level before the interview — the calibration shifts substantially.

Google data engineer compensation

Median and range from verified salary reports, by level.

LevelBaseTotal comp
JuniorL3$130K median$174K median · $118K–$209K · 9 reports
Mid-levelL4$152K median$236K median · $184K–$260K · 13 reports
SeniorL5$174K median$320K median · $227K–$347K · 14 reports
StaffL6$218K median$404K median · $317K–$469K · 11 reports
PrincipalL7$270K median$570K median · $459K–$672K · 7 reports

The Google data stack

What their data engineers work with day to day. Worth brushing up on the heavy hitters before the loop.

Languages

Python9SQL8Java1JavaScript1Scala1

Tools and platforms

Spark10Power BI6Tableau6GCP3Informatica1

Google practice set

Problems on the platform tagged and predicted for Google loops, from live listings and interview reports.

SQLeasy~5 min

Full Customer Order List

Return first_name, last_name, and country for every customer in customers. Sort alphabetically by first_name, then last_name.

Pythonmedium~10 min

Detect Cycle in Sequence

You are given a list of integers where each value at index i is the next index to visit (or -1 to terminate). Starting from index 0, follow the chain and return True if you revisit any index, False otherwise. Out-of-range indices (including -1) count as termination, not a cycle.

Data Modelingeasy~15 min

The Balance Always Reconciles

We're a consumer lending company that offers personal loans, auto loans, and mortgages. Customers make monthly payments, but sometimes they pay early, miss payments, or refinance. The operations team needs outstanding balances and the risk team needs to flag delinquent accounts. Can you design the schema?

SQLeasy~5 min

High Volume Batch Jobs

Surface all batch jobs that processed more than 5000 rows, showing each job's name, priority, and rows processed, ranked from most to fewest.

SQLmedium~5 min

Active Duo

The growth team is building a cross-engagement segment of users who both make purchases and log browsing sessions on the platform. Return a deduplicated list of usernames for users with activity in both areas.

Pythoneasy~10 min

Quantile Calculator

Given a list of numbers and percentile (0-100), return the value at that percentile using linear interpolation. The index is percentile / 100 * (n - 1); if fractional, linearly interpolate between the floor and ceiling indices of the sorted values.

Google DE interview FAQ

How many rounds are in a Google DE interview?+
Typically 5 to 6: recruiter screen, technical phone screen, and 3 to 4 onsite rounds (2 coding, 1 system design, 1 Googleyness/leadership). Some teams add a hiring manager chat as a final step.
Does Google test algorithms for data engineer roles?+
More than Meta or Amazon. Google DE interviews may include Python problems that require efficient data structures (heaps, hash maps) or algorithmic thinking. It is not full LeetCode hard, but it is more than basic scripting. Prepare for medium-difficulty algorithm problems with a data engineering flavor.
What SQL engine does Google use?+
BigQuery for analytics, Spanner for transactional workloads. In interviews, BigQuery SQL is the default context. Know BigQuery-specific features: STRUCT, ARRAY, UNNEST, APPROX_COUNT_DISTINCT, and partitioned/clustered tables.
What level are most Google DE roles?+
Google uses L3 (entry), L4 (mid), L5 (senior), L6 (staff). Most external hires come in at L4 or L5. L3 interviews focus on coding and basic SQL. L5 adds system design depth and cross-team impact. L6 requires org-level influence and architectural decisions.
How important is the Googleyness round?+
It matters. A strong Googleyness signal can tip a borderline case in your favor. A weak signal can sink an otherwise strong technical performance. The committee weighs it alongside technical feedback. Prepare 3 to 4 specific stories demonstrating collaboration, curiosity, and humility.
Does Google have a take-home component for DE roles?+
Almost never. Google's process is built around live interviews with structured rubrics. Some teams (especially Cloud) may include a small architecture exercise during the recruiter coordination phase, but it's the exception. Plan for a live-coding-heavy loop.
02 / Why practice

Prepare for Google's technical bar

  1. 01

    Active recall beats re-reading by 50%

    Cognitive-science meta-reviews (Dunlosky et al., 2013) rank practice testing as a top-tier study technique, while re-reading and highlighting rank near the bottom

  2. 02

    76% of hiring managers reject on the coding task, not the resume

    From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice

  3. 03

    Five problem shapes cover 80% of data engineer loops

    Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition

Practice SQL and Python

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