Senior Data Engineer

Role Objectives:The Senior Data Engineer owns complex, end-to-end data engineering initiatives within a business stream, from problem definition and solution design through production delivery and ongoing operation. The role is responsible for building reliable and consistent data products, defining data quality and freshness standards, and solving complex data modeling and pipeline challenges. The Senior Data Engineer also provides technical leadership to other engineers, drives engineering standards and reusable practices, and leads complex production issues and cross-functional initiatives to ensure scalable, reliable, and high-quality data solutions.The warehouse runs across BigQuery and a Trino + Apache Iceberg lakehouse, which keeps the design questions at this level genuinely hard.Roles & Responsibilities:Own complex, ambiguous data engineering initiatives for a business stream, from problem definition and solution design through production delivery and ongoing operation.Design and maintain stream-level data products, including data models, contracts, quality controls, and consistent business definitions across domains and analytical use cases.Define and maintain appropriate data quality, freshness, reliability, and service expectations for the stream, and drive performance against them.Lead technical planning for major changes and migrations, considering dependencies, sequencing, rollback strategies, and transition costs.Lead complex production incidents, coordinating resolution, root-cause analysis, and preventative improvements across relevant teams.Solve complex data engineering challenges involving incremental processing, data skew, large-scale fact data, performance, and semantic consistency across domains.Mentor engineers, provide technical guidance, raise the standard of code reviews, and develop reusable engineering patterns and practices adopted across the team.Required Education, Knowledge, And Skills:Expert SQL and a working understanding of how queries execute. Plans, join strategies, skew, spill, concurrency.+5 years of experience in data engineering or an adjacent production engineering role.Strong Python.Data modelling and semantic design at stream scale. Contracts, conformed dimensions, slowly changing dimensions, incremental strategies.dbt or an equivalent framework at scale, including macros and a deliberate testing strategy.Orchestration in production, including partitioning, backfills and dependency management.Git, code review and CI.Docker, and enough Kubernetes to reason about where your workloads run and why they fail.4-7 years in data engineering.