Senior Data Engineer

Role overview Own the end-to-end data platform that powers analytics, business intelligence, AI agents, and executive decision-making for a fast-growing product-led business. This is a senior, hands-on engineering role reporting into a central data function, focused on ingestion, orchestration, reliability, and governance rather than one-off reporting.

Responsibilities - Build and operate ingestion through managed connectors and custom extraction, owning how every new source lands in the warehouse. - Run orchestration across the modern data stack (dbt-style transformation tooling and GitHub-based workflows), move critical models from nightly full rebuilds to incremental patterns, and reduce decision latency. - Stand up schema-change detection, freshness SLAs, and failure/drift alerting so broken pipelines surface before downstream consumers notice. - Serve as a first responder when pipelines fail and drive structural fixes upstream so the same incident does not recur. - Manage compute cost and make freshness-versus-spend tradeoffs explicit rather than accidental. - Share warehouse administration: role-based access, security and network policies, PII masking, storage organization, and Terraform-managed infrastructure. - Contribute foundational modeling patterns (source-to-staging, conformed dimensions, shared entities, SCD) that analytics engineers build on. - Build development environments, CI, and onboarding tooling so new engineers and analysts are productive in days, and review contributions to keep quality high.

Requirements - 5+ years of data engineering experience owning production systems other teams depended on. - Strong Python and SQL with production experience across ingestion (e.g., Fivetran-class tools), orchestration (dbt, GitHub Actions, or Airflow), and cloud infrastructure. - Solid warehouse depth: access control, warehouse sizing, query performance, and cost management. - Practical dbt skills including incremental models, testing, macros, git-based workflows with CI, and building SCD tables from multiple sources. - Comfort working in a Terraform-managed environment where infrastructure changes go through code review. - Hands-on experience building reliability practice from scratch: alerting, freshness SLAs, incident response, and schema change detection. - Proficiency with AI-assisted development, including agentic pipeline design and skill-based workflows, plus comfort integrating tools via MCP-style servers. - Ability to explain to a non-technical stakeholder what broke, what it affected, and when it will be fixed. - Comfort building where the playbook does not yet exist.

Nice to have - Experience in regulated or high-sensitivity data environments (legal, healthcare, financial services). - Exposure to streaming or near-real-time ingestion, including judgment about when it is not worth the cost. - Familiarity with Iceberg, Parquet, or unstructured data at scale. - B2B SaaS experience, particularly serving small and mid-sized or professional services customers.

Benefits and work setup - US base salary range of $185,000–$245,000 plus equity. - Competitive compensation with equity participation. - 401(k) program with employer matching. - Health, dental, vision, and life insurance, plus short- and long-term disability coverage. - Commuter benefits for in-office employees. - Autonomous work environment with workplace setup reimbursement and a telecomm stipend. - Flexible time off plus holidays, and quarterly team gatherings.