Google Data Platform Architect

• Job Title: Google Data Platform Architect
• Location: Mountain View, CA/Remote

Responsibilities

• Own the end-to-end target architecture across ingestion, raw, conformed, and consumption layers, and maintain it as new sources are onboarded.
• Define the source onboarding pattern and extend it to new systems, working with client teams and third-party system integrators who own those systems.
• Govern Pub/Sub schemas as enforced data contracts, including evolution and versioning policy across independently owned sources.
• Own the streaming correctness design: ordering, idempotency, exactly-once semantics, dead letter queues, and an operator-usable replay and reprocess mechanism.
• Own the Dataform architecture across conformance and aggregation, the assertion-based data quality approach, and the dimensional model for business-ready marts.
• Design the governance and security model: Dataplex zones, IAM, policy tags, column- and row-level security, VPC Service Controls, CMEK, and DLP coverage.
• Own BigQuery and Dataflow cost architecture, including the always-on streaming footprint.

• Pub/Sub backlog, Dataflow lag, Dataform assertions and BigQuery telemetry;
• Dataplex-integrated governance; self-healing patterns with explicit escalation boundaries;
• and cost attribution by data product.
• Secure client architecture, security, and privacy approvals; mentor the delivery pod and enforce standards through review.

• Required
• Proven experience architecting event-driven streaming platforms, not batch ETL scaled up — Pub/Sub, Dataflow / Apache Beam, and the operational realities of always-on streaming.
• Command of streaming correctness: ordering, idempotency, exactly-once, late and duplicate events, dead letter and replay design.
• Deep BigQuery expertise — modeling, tuning, partitioning and clustering, reservations, cost optimization.
• Dimensional modeling depth: facts, dimensions, conformed dimensions across domains.
• Dataform or equivalent in-warehouse transformation framework, with dependency management and assertion-based quality.
• Dataplex, DLP, and fine-grained BigQuery access control.
• Experience integrating enterprise SaaS systems as event sources and their change-event mechanisms.
• Strong SQL and Python; Terraform; CI/CD for data pipelines.
• Ability to run technical review with enterprise architects and security teams and defend design decisions.

Preferred

• GCP Professional Data Engineer or Professional Cloud Architect certification.
• Public sector or regulated-industry delivery experience.
• Analytics Hub and Looker / LookML; DataOps or observability tooling; Vertex AI and MLOps exposure.