A
Senior Data Engineer
Not specifiedFull-timeData EngineeringAdTech
Own the technical direction of data pipelines that process billions of web events. Lead design and planning, partner with product and engineering teams, and build reliable systems with strong freshness and accuracy.
Responsibilities
- Build and improve end-to-end pipelines for web sessions, events, and conversions, from ingestion through distributed processing to data serving
- Meet strict data freshness and accuracy service-level objectives
- Lead pipeline technical design from specification through production, write design documents, review designs, and make implementation trade-offs
- Collaborate with architects on cross-team designs
- Partner with the team lead on roadmaps, planning, priorities, and project breakdown
- Represent the team in technical discussions with product, research and development, and partner teams
- Own production incidents through resolution and implement lasting fixes
Requirements
- At least 6 years of data or big data engineering experience, including production systems at scale
- Bachelor’s degree in computer science or equivalent
- Deep Apache Spark expertise, including execution plans, partitioning, memory management, and performance debugging
- Extensive experience with a cloud analytical warehouse such as BigQuery, Snowflake, Redshift, or Databricks SQL
- Experience designing data systems, documenting decisions, weighing cost, latency, and correctness, and delivering designs to production
- Experience with workflow orchestration and production-scale cloud environments
- Strong programming fundamentals and experience writing clean, testable production code
- Hands-on use of AI tools in engineering work and informed judgment about their application to data engineering
- Scala experience preferred; Python experience for orchestration
Nice to have
- BigQuery and GCP experience
- Advanced Scala or functional programming
- Experience with attribution systems, models, or AdTech data
- Experience with Dataform, dbt, data mesh, or data-product platforms
- Kafka or other streaming systems experience
- Experience operating LLMs or machine-learning pipelines in production