Senior Data Platform Engineer

Requisitos e responsabilidades

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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. 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.

Qualifications

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 haves

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).

Perks & Benefits

Comprehensive health coverage including medical, dental, vision, and mental health resources
401(k) Plan
Equity award
Flexible time off
Paid parental leave
Annual Company Event
WFH Equipment Stipend

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