Senior Data Engineer

Manage pipeline compute cost, and make the tradeoffs between freshness and spend explicit rather than accidental

Share Snowflake administration: implementing the role-based access model, security and network policies, data masking and PII controls, and storage organization

Extend the medallion architecture and the Terraform-managed footprint, including full separation of development and production

Contribute to the foundational modeling layer the Analytics Engineers build on: source-to-staging patterns, conformed dimensions, shared entities, and SCD patterns that make history reliable

Raise the bar

Build and maintain the development environments and CI that let analysts contribute models safely, and review their contributions so more of the company can build on the foundation

Build the tooling and setup that gets a new engineer or analyst productive in days, not weeks

Administer the data tooling stack: access, integrations, and the connective work between systems

Use AI-assisted development as part of how you work: Claude Code skills, agents, and evals, held to the same review bar as anything else

Document as you build. If it isn't written down, it isn't done

What We're Looking For

5+ years in data engineering, owning production systems other people depended on

Strong Python and SQL, with production experience across ingestion (Fivetran or similar), orchestration (dbt platform, GitHub Actions, or Airflow), and cloud infrastructure

Solid Snowflake: access control, warehouse sizing, query performance, and cost management

Practical dbt: incremental models, testing, macros, and a git-based workflow with CI. Experience building SCD tables from multiple sources

Comfort in a Terraform-managed environment. Infrastructure changes go through code review here, not the Snowflake console

You've built reliability practice where none existed: alerting, freshness SLAs, incident response, schema change detection

Proficiency with AI-assisted development such as Claude Code, including agentic pipeline design and skill-based workflows, and comfort integrating tools via MCP servers

Able to tell a stakeholder what broke, what it affected, and when it'll be fixed, without the jargon

Comfort building where the playbook doesn't exist yet

Nice to haves:

Experience in a regulated or high-sensitivity data environment (legal, healthcare, financial services)

Experience with streaming or near-real-time ingestion, and knowing when it isn't worth it

Exposure to Iceberg, Parquet, or unstructured data at scale

B2B SaaS, especially selling to small and mid-sized businesses or professional services firms