Technical Lead - Data Engineering

Apply if you’re excited to:

Leads technical design for assigned workstreams end-to-end, producing written design proposals for review before implementation begins.
Builds and maintains production models and pipelines in dbt Core, Snowflake SQL, and Python, remaining hands-on in the codebase most of the working week.
Investigates source data directly to derive modeling requirements and confirm grain and join logic where specifications are incomplete.
Reviews pull requests daily, enforcing modeling conventions, test coverage, and performance standards.
Owns reliability of assigned pipelines, diagnosing failures and implementing tests and alerting that catch data issues before stakeholders do.
Mentors engineers through pairing and design feedback, growing local ownership of the platform over time.
Coordinates daily with engineers and stakeholders across regions, translating ambiguous business questions into scoped work.

About you:

8+ years in data or software engineering, including 2+ years as a technical lead, architect, or owner of a major data platform workstream.
Expert SQL, including analytical functions and performance tuning in a cloud data warehouse; Snowflake strongly preferred.
Deep production dbt experience (Core preferred): multi-repo project structure, testing strategy, macros, incremental models, CI integration.
Strong Python for pipeline code, API integrations, and orchestration in Airflow or equivalent.
Proven ability to work from raw, undocumented source systems and propose a defensible model without being handed requirements.
Working knowledge of ingestion tooling (Airbyte, Fivetran, or similar), CDC patterns, and relational sources such as PostgreSQL and SQL Server.
Git-based workflows, CI/CD (Buildkite, GitHub Actions, or similar), and scripting or IaC on AWS.
Track record of raising team engineering standards in ways that outlasted a single project.
Clear written and spoken English; documents decisions well and disagrees constructively in review.
Self-directed and comfortable with ambiguity; makes progress and escalates appropriately when other team members are offline.