Coinbase · coinbase.com · checked today
Staff Analytics Engineer
Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way.
Skills, with evidence
- Airflow / orchestration
Built and owned production ETL/ELT pipelines with dbt, Airflow or similar, including testing, monitoring and SLAs.
must have - Data modelling
Deep data modeling: designed modular, reusable source-of-truth models (star/snowflake schemas, SCDs) used by many teams.
must have - Python
Expert SQL (window functions, complex aggregations, optimization) and strong Python (OOP, reusable frameworks). No production ETL or SQL experience = reject.
must have - SQL
Expert SQL (window functions, complex aggregations, optimization) and strong Python (OOP, reusable frameworks). No production ETL or SQL experience = reject.
must have - Warehousing
Modern data warehouse/lakehouse experience (Snowflake, Databricks, BigQuery, Redshift). Snowflake is not required; no comparable warehouse experience = reject.
must have - dbt
Built and owned production ETL/ELT pipelines with dbt, Airflow or similar, including testing, monitoring and SLAs.
must have - Data quality
We sit at the intersection of data engineering, data science and business analytics: we own the source-of-truth financial datasets in Snowflake/Databricks, the pipelines that produce them, and the reconciliation, month-end close, safeguarding and reporting workflows built on top.
- Failure handling
Define reusable data quality and reconciliation frameworks (tests, monitoring, alerting, SLAs) and agree with Accounting on what "done" means for financial correctness.
- Idempotency & backfills
Build and own scalable batch (and where needed, near-real-time) pipelines orchestrated with Airflow/dbt, including dependency management, backfills and runbooks.
- Schema evolution & contracts
Drive data contracts with upstream engineering teams, mentor Analytics Engineers, and build abstractions (Python packages, UDFs, LLM-assisted tools) that make finance data users more efficient.
- Spark
Modern data warehouse/lakehouse experience (Snowflake, Databricks, BigQuery, Redshift).
- GCP
Modern data warehouse/lakehouse experience (Snowflake, Databricks, BigQuery, Redshift).
not practised here - Governance & security
Make financial data and pipelines audit-ready: SOX-compliant controls, lineage, change management and evidence for internal and external auditors.
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
SQL · Data quality
- Airbnb: converting on a day with no published rateAdvanced
- Amazon: net revenue across two fan-outsAdvanced
- Build a percentile tableAdvanced
- Duplicate snapshot keysAdvanced
- Flag outlier ordersAdvanced
- Python: the data-wrangling round≈ 4 h
Python
- Data modelling: the round most people fail≈ 4 h
Data modelling · Warehousing · dbt
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration · Failure handling · Idempotency & backfills · Schema evolution & contracts
- Fourteen months of wrong numbersAdvanced
- The payload changed overnightAdvanced
- Delete me from everywhereAdvanced
- You fixed it. Who already used it?Advanced
- The source that liesAdvanced
- Spark: read the plan Spark actually ran≈ 2 h
Spark
- applyInPandas per country: what Spark ships to Python, and the native rewriteAdvanced
- Filters you wrote in the wrong place: where Catalyst moves themAdvanced
- snappy, gzip or zstd: measure the Parquet codec trade-off yourselfAdvanced
- A correlated COUNT(*) subquery: the join Spark runs, and the count bug it avoidsAdvanced
- Find the four suspects in a slow pipeline (one of them is innocent)Advanced
- Say it out loud≈ 1 h
25 drills · Advanced + Intermediate≈ 17 hours
Not covered by the plan: GCP, Governance & security.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 6 dremoved the moment Coinbase closes it