Senior Google Data Platform Engineer || Remote

Senior Google Data Platform Engineer

8–12 years data engineering, including 4+ years hands-on Google Cloud Platform. Reports to the Platform Architect.

Leads the build of one or more source domains and acts as technical lead for a pod of two to four engineers. Owns delivery within the architect's frame; does not own platform-wide design or the DataOps layer.

Responsibilities

·        Own a source domain end to end: Pub/Sub consumption, Dataflow ingestion, bronze landing, Dataform conformance, and the resulting data mart.

·        Confirm the source-side publishing contract with system owners and third-party integrators, applying the defined onboarding pattern.

·        Build streaming pipelines handling ordering, idempotency, deduplication, and late-arriving events; implement and prove DLQ, archival, and replay.

·        Build conformed and mart-layer Dataform models with assertions covering agreed data quality rules; conform shared dimensions rather than forking them.

·        Build and operate reconciliation against the system of record and produce the evidence package for sign-off.

·        Apply Dataplex registration, policy tags, and row-level security across the domain.

·        Lead the pod: assign work, review code, hold the quality bar, and mentor on streaming concepts.

·        Translate the target-state design into an executable build plan; escalate architectural conflicts early rather than coding around them.

·        Ensure every pipeline emits structured logs and metrics so the platform's operations layer can monitor it; write runbooks and lead hypercare for the domain.

Required

·        Production streaming experience — Pub/Sub and Dataflow, or Kafka / Flink / Kinesis — including deduplication, ordering, and replay.

·        Strong Python, advanced SQL, and Apache Beam.

·        Deep hands-on BigQuery: partitioning, clustering, incremental merge patterns, cost-aware design.

·        Dimensional modeling built in a real warehouse, including conformed dimensions.

·        Dataform or dbt at production scale with tests or assertions and dependency management.

·        Terraform, Git workflow, and CI/CD for data pipelines.

·        Experience integrating a major SaaS platform as a data source.

·        Track record leading a small team or owning a workstream, with judgment on which decisions are theirs and which belong to the architect.

Must Have

·        Google Cloud Platform Professional Data Engineer certification; Dataplex and DLP; Analytics Hub or Looker; public sector delivery experience.