Remote job
Staff Data Platform Engineer
Job details
About this role
Role overview
Serve as the founding engineer for a data platform team responsible for making company-wide data products discoverable, reliable, observable, and easier to operate. You will build shared platform capabilities, define engineering standards, enable teams to publish data safely, and establish the technical direction for a long-term platform supporting high-volume, multi-channel event data and automated troubleshooting.
Responsibilities
- Build a data control plane covering schemas, lineage, ownership, freshness commitments, quality, access, and discoverability. - Create reusable libraries, SDKs, templates, and data-specific CI/CD workflows that make the standard path easy to adopt. - Develop a supported near-real-time data pattern and help teams evolve beyond bespoke, team-specific pipelines. - Implement enforcement mechanisms such as a schema registry, contract validation, CI checks, and automated certification. - Make data products observable by default, including freshness, volume, schema conformance, and quality signals connected to ownership and lineage metadata. - Set architecture, roadmap, and build-versus-buy direction through design reviews, mentorship, and collaboration with data-producing and data-consuming teams.
Requirements
- Experience building loosely coupled production services, APIs, and Python SDKs used by multiple teams. - Track record creating metadata-driven platforms that integrate with catalogs and automatically check lineage, contracts, and quality. - Experience building observability systems that other teams rely on, including the judgment to distinguish useful signals from noise. - Ability to drive adoption across teams without direct authority through architecture reviews, mentorship, and clear communication. - Strong experience with AWS, Terraform, and Kubernetes.
Nice to have
- Experience moving data workloads from batch to streaming and understanding the associated cost tradeoffs. - Experience with Spark and Iceberg at significant scale, Snowflake in production, or high-volume event-data environments. - Experience building tools and data structures for AI agents, data catalogs, semantic layers, governance systems, or a first-generation platform team.