Lead Data Engineer (Revenue Operations)

Join Stream as a Lead Data Engineer in Revenue Operations. You'll own the data pipelines, integrations, and central repository that drive our go-to-market and product decisions. As we migrate to GCP, you'll shape the architecture and build durable systems. This role focuses on data modeling, improving reliability, and enabling the business with trusted datasets. Enjoy benefits like stock options, health coverage, and a monthly fitness stipend.

• Lead the design, development, and maintenance of data pipelines, integrations, and central repository to ensure reliable and accurate data for business decisions.
• Collaborate with the Revenue Operations team to understand business requirements and translate them into durable systems, focusing on building and evolving the ingestion platform.
• Take ownership of the platform infrastructure, including BigQuery and supporting GCP, ensuring security, reliability, and cost-effectiveness.

• Experience designing incremental, idempotent, well-tested pipelines
• Experience with orchestration and CI/CD (GitHub Actions, Airflow, or equivalent)
• Experience with modern ELT tooling such as SQLMesh, dbt, dltHub, Fivetran, or Airbyte
• Solid experience with BigQuery or another modern cloud data warehouse
• 5+ years building and operating production data platforms
• Expert SQL and strong Python
• Strong data modeling skills: dimensional modeling, warehouse design, testing, and observability
• You like owning a platform end to end and staying hands-on while you do it. You're comfortable in a small team, and you're energized by building rather than by growing an org around you. You influence through the work: architecture, code review, and clear conventions, not a title
• Infrastructure-as-code experience with Terraform or a close equivalent
• Product analytics platforms such as PostHog
• Revenue Operations or GTM data experience
• Salesforce and Stripe data modeling
• Marketing attribution and funnel analytics
• MRR, expansion, contraction, churn, and revenue reconciliation logic
• GCP depth, including IAM, service accounts, and BigQuery cost optimization
• Working closely with business stakeholders while keeping engineering discipline