Senior Google Data Platform Engineer || Remote
K&K Global Talent Solutions
- Job
- 28133
- Posted
- Location
- Remote
- Work type
- Contract
- Tax terms
- C2C
- Experience
- Experience open
- Openings
- 1 opening
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Skills
- Kafka
- SQL
- Python
- dbt
- BigQuery
- Looker
- Terraform
- CI/CD
About the job
Job: Senior Google Data Platform Engineer
Location: Remote
10 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.