Senior Data Platform Engineer

Responsibilities

• Own and build the ingestion layer. Design, deploy, and scale pipelines that pull from third-party APIs, internal services, and SaaS tools into BigQuery.
• 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.
• 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.
• 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.

Requirements

• 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.

Nice to Have

• Production DBT experience: layered models, tests, snapshots, macros, deferred builds.
• Experience working with a semantic layer, metrics layer (DBT Semantic Layer, Cube, LookML).
• Comfortable with exploratory analysis, designing experiments and A/B tests, basic statistical modeling, and separating signal from noise in messy data.
• Exposure to building AI agents or applications.
• Infrastructure-as-code (Terraform, Pulumi, or similar).