Staff Data Engineer
Technical leader on the Data Engineering team driving design and implementation of foundational data products and analytics platform capabilities. Owns complex data systems end-to-end and leads platform modernization, migrations, and cross-functional initiatives.
Responsibilities
- Lead platform modernization efforts across the analytics ecosystem, including deprecating legacy workflows, migrating pipelines to scalable patterns, and improving infrastructure, CI/CD, and developer experience.
- Drive high-leverage infrastructure and FinOps initiatives across systems like Snowflake, Dagster, and dbt to reduce cost, improve governance, and increase scalability.
- Own platform evolution projects such as making data more consumable by agentic tools and improving orchestration tooling.
- Design and deliver complex, domain-critical data products used by analysts, data scientists, and product teams.
- Architect scalable patterns for modeling, orchestration, and data transformation.
- Lead technical planning and delivery across cross-functional teams.
- Drive platform adoption and best practices, mentoring engineers and building documentation.
- Partner with engineering, product, data science, and business stakeholders.
- Represent Data Engineering in technical design forums and roadmap discussions.
Requirements
- 8+ years of experience in data or analytics engineering with a track record of owning complex, business-critical data systems end to end.
- Deep experience with the modern data stack (Snowflake, dbt, Dagster, Databricks), Terraform, and cloud infrastructure.
- Track record of leading platform migrations, deprecations, or upgrades.
- Experience designing secure, reusable patterns for data ingestion, access control, and platform automation.
- Experience with DevOps practices (CI/CD for data), data governance, or FinOps.
- Ability to break down ambiguous, cross-functional data problems and lead implementation from design to deployment.
- Strong communication skills across technical and non-technical audiences.
- Excitement about mentoring others and improving systems.
Nice-to-Haves
- Experience supporting machine learning workflows.
- Experience in a fast-growing startup environment or on platform-style teams.
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