Lead Data Engineer

What You'll Do:

Architect and optimize large-scale data platforms on Google Cloud, with BigQuery as the analytical backbone

Design and build unified batch and streaming pipelines that handle high-volume, mission-critical workloads

Lead infrastructure-as-code practices, ensuring environments are repeatable, secure, and version-controlled

Implement open table formats to enable cross-cloud and cross-engine data interoperability

Establish automated data quality, metadata, and lineage practices across the data estate

Partner with data scientists, analysts, and product teams to translate business needs into reliable data products

Mentor engineers, review designs, and raise the bar on engineering standards

Must-Have Skills:

7+ years in data engineering, with at least 2 years in a lead or senior individual contributor capacity on Google Cloud-based platforms

BigQuery (Advanced): Deep knowledge of BigQuery architecture, including partitioning, clustering, slot management, storage optimization, and query execution tuning

Streaming & Batch Pipelines: Strong hands-on experience building unified pipelines using Dataflow (Apache Beam), Dataproc , and Pub/Sub

Infrastructure as Code: Production experience developing and managing cloud infrastructure with Terraform

Open Table Formats: Working knowledge of Apache Iceberg , including its role in enabling cloud and engine interoperability (e.g., across BigQuery, Spark, Snowflake)

Data Governance: Experience with Dataplex and Data Catalog for automated data quality checks, metadata tagging, and column-level lineage from source to destination

Nice-to-Have Skills:

Experience leading or mentoring data engineering teams

Familiarity with CI/CD for data pipelines (Cloud Build, GitHub Actions)

Exposure to multi-cloud or hybrid data architectures

Background in regulated industries (healthcare, financial services) where governance and lineage are critical