Senior Data Engineer

Our dashboards are slow. Pipelines break. Analysts can't trust the data. Data scientists wait days for features.

You fix that. As a Senior Data Engineer, you build the pipes, warehouses, and tools that let 2000+ people make data-driven decisions at petabyte scale. If data is late, wrong, or expensive — you feel it. If queries run 10x faster and costs drop 50% — that's also you.

What You'll Do

1. Build & Scale Data Pipelines

• Design, build, and operate batch + real-time pipelines ingesting 1M+ events/sec from app, logs, and 3rd party sources
• Own data modeling: dimensional, Data Vault, or lakehouse on Iceberg/Hudi/Delta. You decide, you defend
• Transform messy raw data into clean, tested, documented marts that 1000+ users trust
• Orchestrate 1000+ DAGs with Airflow: SLAs, retries, backfills, dependency management

2. Platform & Infrastructure

• Optimize Spark/Flink jobs that process TBs daily. You know when to cache, partition, and when to rewrite in SQL
• Improve query performance: BigQuery, Snowflake, Trino, or ClickHouse. Sub-second SLAs on billion-row tables
• Build self-serve tooling: data ingestion frameworks, CI/CD for data, testing harnesses
• Manage data infra: Kafka, Spark, Airflow, dbt. Upgrades, scaling, cost optimization

3. Data Quality & Reliability

• Implement data contracts, schema evolution, and breaking change detection with producers
• Build observability: freshness, volume, schema, and distribution checks. Great Expectations, Monte Carlo, or custom
• Own lineage and cataloging. If someone asks where did this number come from?, you can answer in 30 sec
• Oncall for critical pipelines. P0 means P0. You've debugged 3am Airflow failures before

4. Enable the Business

• Partner with Analytics Eng, DS, and Product to understand data needs and ship solutions fast
• Translate vague requests like I need user data into versioned, tested, documented datasets
• Mentor mid-level DEs. Review designs, PRs, and raise the bar for data engineering
• Kill tech debt. Deprecate unused tables. Archive cold data. FinOps is part of the job

What You'll Bring

Must-haves:

• 5+ YOE as a Data Engineer with production experience at TB–PB scale
• Expert SQL: Window functions, CTEs, query plans. You can make a 30min query run in 30s
• Python + Spark: PySpark, DataFrames, UDFs, performance tuning. You know why your job OOMed
• Data modeling: Star schema, slowly changing dimensions, idempotency. You've been burned by bad models before
• Warehouse/Lakehouse: Deep experience with BigQuery, Snowflake, Redshift, or Iceberg/Hudi/Delta
• Orchestration: Airflow, Dagster, or Prefect. You've built complex DAGs and suffered through timezone bugs
• Software engineering: Git, CI/CD, testing, code reviews. You don't ship untested SQL
• Systems thinking: You consider cost, latency, freshness, and downstream impact in every design