Google Data Engineer Interview GuideGoogle's loop carries 2 onsite coding rounds, more than Meta or Amazon, and expects algorithmic thinking (heaps, hash maps, complexity awareness) alongside SQL and ETL logic. System design is pitched at Google scale, and a dedicated Googleyness round evaluates collaboration and intellectual humility. The defining structural difference is the hiring committee: interviewers submit feedback packets and a committee, not the interviewer, decides.
First contact to offer, stage by stage, with how long each round runs and roughly when it lands.
The loop stage by stage, from recruiter call to offer.
Non-technical call. The recruiter reviews your background, explains the process, and checks role fit. Google has DE roles across Ads, Cloud (the BigQuery team), YouTube, Search, and Waymo. Ask which team the role is for; the technical expectations vary significantly. The recruiter also assigns a target level (L3 through L6) from your years of experience and past scope, but that target is not final: your interview performance sets the actual offer level.
1 or 2 coding problems, usually in SQL or Python, done in a shared editor (a Google Doc or similar, not a full IDE, so no autocomplete or syntax highlighting). Google values algorithmic thinking more than other companies for DE roles. Expect a SQL problem needing window functions, self-joins, or date manipulation, followed by a discussion of how you would optimize it at scale, or a Python problem focused on data transformation.
SQL or Python coding, focused on data processing or manipulation. 1 or 2 problems at intermediate to advanced difficulty. If SQL, expect window functions, complex joins, and optimization discussion. If Python, expect data-processing logic: parsing CSV files, transforming nested JSON, deduplicating records, or building a simple aggregation pipeline, with proper error handling. Google interviewers assess code quality, not just correctness. Clean variable names, comments on tricky logic, and handling edge cases all matter, and they follow up on how your solution scales.
2 to 3 SQL problems of increasing difficulty, often in Google-scale contexts: ad impressions, search queries, YouTube views, or Cloud billing. Write correct SQL, explain your approach step by step, and discuss optimization. After the query you are often asked to design or critique a schema: how would you model this for analytics vs transactional use, or what indexes would make this query fast on a 5-billion-row table. Some teams fold this into a second coding round instead, sometimes with a different language focus than the first.
Design a data pipeline or data platform component for a Google-scale use case: a YouTube video-analytics pipeline, Search query-log processing, an Ads attribution or data-quality monitoring system, or a real-time feature store. You are expected to drive the conversation: ask clarifying questions, sketch the architecture, estimate data volumes, choose technologies, and reason about scale (the largest in the industry), fault tolerance, exactly-once semantics, and data freshness.
Google's behavioral round. 'Googleyness' is Google's term for intellectual humility, comfort with ambiguity, a collaborative mindset, and a bias toward action. Expect questions like 'tell me about a time you disagreed with a technical decision,' 'describe a project where requirements changed significantly,' or 'how did you handle a decision without enough information.' Unlike Amazon's LP-heavy approach it is one dedicated session, but it matters: a strong Googleyness rating can compensate for a borderline technical round.
Our prediction of the question mix by domain and difficulty for this company's data engineer loop, from live listings and interview reports.
The domain and difficulty mix we predict for a Google data engineer loop, across 14 problems. It updates as more Google data lands.
Reported by candidates from real loops, tagged by domain, round, level, and year. Expand for what the round is scoring.
Extract phone numbers from free-text customer_response using pattern matching (LIKE or regex), then aggregate by employee and rank to find top 10.
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.
Company: Google Role: Cloud Data Engineer YOE: 9yrs Round: Phone Interview Location: USA, Remote there was two questions: 1. 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. 2. Second was an algorithm as mentioned below. I used python to write it.
How hard candidates rated the loop and how they felt, summarized across the reports below.
5 rated reports
Candidate accounts of the loop, each with its date, level, difficulty, and outcome. Scroll the feed.
5 candidate interview reports
real candidate submissions
Hi Everyone, I'm preparing for a Data Engineering interview with Google and would like to know what types of Data Structures and Algorithms (DSA) problems are typically asked. Could you also share what other types of technical and non-technical questions I should be ready for during the interview process?
The problem given to me was a one off, so I would not expect this to come up. I'm also taking some creative liberties to make the problem easier to describe. The hardest part was fishing out requirements to learn that this was a matrix/island problem. Part 1: Q.
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Given a matrix of m x m servers, come up with a process to find bad servers. A. Collect a KPI like percent_cpu_usage from every node, find the average across the entire matrix, then define a bad server as -2 standard deviations from norm. Q. Given that same matrix, find the size of the largest area of bad servers. Assume each node m can talk to a server above, right, below, left of it. A.
Company: Google Role: Cloud Data Engineer YOE: 9yrs Round: Phone Interview Location: USA, Remote there was two questions: 1. 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.
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Question was: find the top movie for the current month based on the gross earning. 2. Second was an algorithm as mentioned below. I used python to write it. ``` Write a standalone program (not a shell script or query) to output the top accessed tables in a database from a system log file: A sample line from the system log file for your database db01: creation_time,job_id,statement_type,referenced_table,statement 1471610471,5dc472,SELECT,db01.table_a,"SELECT * FROM db01.table_a" 1471610663,772da2,INSERT,db01.table_b,"INSERT INTO db01.table_b (col1, col2) VALUES (1,2),(3,4)" A sample output from your program could be for instance: db01.table_a, 1250 db01.table_c, 942 db01.table_b, 701 ``` Update: Got selected for the onsite interview. Find the onsite questions here:
Changing a data quality job into incremental runs which runs for whole data for source and destination have a job which runs every 5 hours and looks for complete data between postgres and redshift. Need to make a job which runs incrementally. The design I have been thinking is to get data in a gap of five hours and then put it in a table cause we want to show the consumers up to what time the data quality checks have been done.
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For example: job 1 starts at 12.00am gmt the data quality persistence table would have started_time,end_time,src_count,dest_count,doescountmatch the next job will take the end time of the previous job as starttime and look for data starttime+5hours and put all the records in the table.
One of the requirement is if source and destination doesnt match then we have to see the last time they matched and get all the data from that time and then again do the count.
join two tables based on others timestamp Table A: username | activity | timestamp A activity1 2020-01-01 01:00:00 A activity2 2020-01-01 02:00:00 A activity3 2020-01-01 03:00:00 B activity1 2020-01-01 02:00:00 B activity2 2020-01-01 03:00:00 B activity3 2020-01-01 04:00:00 Table B: username | clickId | timestamp A click1 2020-01-01 00:00:00 A click2 2020-01-01 01:30:00 A click3 2020-01-01 01:45:00 B click1 2020-01-01 00:00:00 B click2 2020-01-01 00:30:00 B click3 2020-01-01 03:35:00 need to match all the activities in the activities table to the most recent clicks in the clicks table performed by each user click matched to each activity must be the most recent click that occured prior to the activity timestamp Output: A activity1 click1 - since for activity1 click1 is the timestamp smaller than it A activity2 click3 A activity3 click3 B activity1 click2 B activity2 click2 B activity3 click3 I tried multiple queries but didnt work
Find every user active on 3 or more CONSECUTIVE days. This gaps-and-islands shape shows up in nearly every DE SQL round. Edit the query and run it against the seed data.
The problems our model expects in this company's interview, grouped by round. Work the shapes that come up, not the ones that read well on a list.
Tactical advice for the dimensions this company weighs.
Google's coding rounds reward efficiency awareness. Know heaps, hash maps, and sorting and where they show up in data processing (streaming medians, top-K, dedup). Be ready to discuss time and space complexity even when the problem looks like plain ETL.
For Google Cloud or an analytics-heavy team, BigQuery knowledge is expected. Know partitioned tables, clustered tables, nested and repeated fields (STRUCT and ARRAY), UNNEST for flattening, APPROX_COUNT_DISTINCT for cardinality at scale, and how columnar storage affects query and cost design. Cost optimization comes up explicitly.
Google uses L3 (entry), L4 (mid), L5 (senior), and L6 (staff), and most external hires land at L4 or L5. The calibration shifts substantially between them: L3 focuses on coding and basic SQL, L5 adds system-design depth and cross-team impact, L6 requires org-level influence. Ask the recruiter before you prepare.
The behavioral round matters and can tip a borderline case either way. Have 3 to 4 specific stories ready that demonstrate collaboration, curiosity, and humility, ideally with evidence-based decisions and quantified impact. 'I would communicate clearly' does not score.
The signals behind the questions. Shape every answer around these.
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.
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.
Each interviewer writes a detailed feedback packet with ratings, and a hiring committee of senior engineers and managers who never met you reads those packets and makes the hire/no-hire call. Your manager advocates but has no unilateral authority. So every answer and every clarifying question is really writing the packet: be explicit, structured, and legible on paper, not just persuasive in the room. Perform consistently, because 2 weak rounds are very hard to overcome even with 1 excellent round; but 1 weak round rarely disqualifies if the rest are strong.
The committee evaluates 4 dimensions (coding, technical knowledge, system design, Googleyness) and also decides your offer level, which can differ from the level the recruiter targeted. Perform at L5 in system design and behavioral and the committee can upgrade an L4 target; the reverse also happens. Because level drives the compensation band, interview performance has a direct, concrete effect on the offer. Review typically takes 1 to 3 weeks; a request for an extra interview is uncommon but not a bad sign.
Google interviewers evaluate communication directly. 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.
The roles behind this loop. Prep against the levels and locations they are actually filling.
Design and enhance large-scale software solutions that enable AI agents to reason and execute directly where the data lives.
About the jobAs a Customer Engineer (CE) with a specialty in data analytics, you will partner with technical sales teams to differentiate Google Cloud to our customers.
Design, build, and scale innovative data products, including self-serve tools, and automated pipelines.
Own the resolution of ambiguous hurdles that prevent adoption and debug integration issues, optimize inference latency, and architect security layers to turn "demos" into production-ready assets.
Design and maintain pipelines to ingest, clean, and process massive volumes of unstructured data, including business transcripts and support cases, into reliable analytical datasets.
Advocate and implement best practices in data infrastructure, software development, testing, and monitor to ensure the reliability, scalability, and efficiency of Golden Data Pipeline's (GDP's) systems.
Architect and build scalable batch and real-time pipelines that power experimentation, product analytics, and ML/AI training loops.
Architect and implement data migration strategies across various database types, including PostgreSQL, Oracle and Alloy DB.
Architect, build, and maintain data pipelines to ingest, process, and transform logs and signals from various Geo services for scraping detection and analysis.
Create and deliver best practice recommendations, tutorials, blog articles, open-source and sample code, and technical presentations adapting to different levels of key business and technical stakeholders.
Design and maintain pipelines to ingest, clean, and process massive volumes of unstructured data, including business transcripts and support cases, into reliable investigative datasets.
Design, build, and maintain data processing systems and data structures that handle legal content removal and compliance reporting.
Design, develop, and implement highly scalable and reliable data pipelines and infrastructure.
Design, develop, test, deploy, maintain, and enhance large-scale software solutions.
Lead the technical design and execution of secure data onboarding workflows, knowledge graphs, and agentic endpoints capable of sustaining autonomous AI reasoning without human mitigation.
The numbers, tech stack, and team structure live on the company overview.
Level-specific pages: the comp, the bar, and what the loop tests at each seniority.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
How the role, pay, and loop stack up against peer companies.
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
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
5 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