Senior Data Platform Engineer, Pinecone

• 4+ years building and operating data pipelines in production
• Strong SQL, with comfort in BigQuery (or Snowflake/Redshift) writing non-trivial analytical queries, optimizing performance, and reasoning about correctness
• Strong coding skills, with comfort writing ETL/rETL, consuming services and integrations against REST/GraphQL APIs, and producing clean code that others can reuse and maintain
• Experience with a modern orchestrator (Airflow, Dagster, Prefect, or similar) running containerized workloads
• Comfort with Docker, Kubernetes, and modern cloud infrastructure best practices
• Experience integrating systems, pulling data between APIs, databases, and warehouses; handling auth, pagination, schema drift, and incremental loads
• Hands-on experience using AI coding tools (Claude Code, Cursor, or similar) as part of your workflow
• Ability to design, build, and own systems end-to-end in a highly autonomous environment

Desirable

• Pinecone is looking for a Senior Data Engineer to own and grow the systems that power how we understand our business
• You will design and operate the ingest, transform, orchestration, and metrics layers that feed analysts, executives, and the Board, and you will lead the analyses themselves when the question matters enough
• This is a high-ownership role on a small team, with direct exposure to finance, GTM, product, and the executive staff
• Own and build the ingestion layer. Design, deploy, and scale pipelines that pull from third-party APIs, internal services, and SaaS tools into BigQuery. Add new sources as the business demands
• Own and build the transform layer. Develop and maintain our DBT project, including staging, intermediate, and marts. Maintain core business datasets: users, organizations, indexes, accounts, usage, revenue. Write tests, snapshots, and documentation. Drive data quality and trust
• Own and build the orchestration platform. Operate the Airflow-on-Kubernetes environment that runs our ingest and DBT workloads. Improve reliability, scalability, observability, and CI/CD
• Establish and maintain the business-context and metrics layer. Curate metric definitions and documentation that feed both human analysts and agents
• Manage infrastructure cost and performance. Manage BigQuery, GKE, Cloud Run, and Kafka costs, right-size compute, and make sure the platform stays efficient
• Lead and own mission-critical company-level analyses. Partner with finance, GTM, product, and exec stakeholders to answer business questions, design metrics, run experiments and evaluations, build views in BI tools, and ship dashboards that support key business decisions as well as regular reporting to the Board of Directors
• Enable other teams to self-serve. Onboard analysts and non-DE stakeholders onto the warehouse, help them with best practices, and create reusable models and tooling
• Set the standard for AI-assisted data workflow. Establish best AI practices and patterns that enable a small data team to operate with outsized leverage