Lead Data Engineer

Responsibilities

• Build and evolve the ingestion platform:
  Python/dltHub pipelines loading into Big Query, integrating Salesforce, Stripe, Postgres, Post Hog, cloud billing, and other GTM systems.
• Build the transformation layer: SQLMesh models across our layered architecture, clean and well‑tested dimensional models, and clear conventions for grain, naming, and audit.
• Improve reliability: expand data quality and observability, build freshness checks, reconciliation tests, and execution monitoring.
• Own the platform infrastructure:
  Big Query and supporting GCP, plus Terraform, IAM, service accounts, scheduled jobs, etc.
• Enable the business: deliver trusted datasets to Looker Studio, Google Sheets, and our internal CRM.
• Set technical direction: define engineering standards and architecture, review pipeline and model changes, and mentor engineers and analysts.

• 6+ years building and operating production data platforms
• Expert SQL and strong Python
• Experience designing incremental, idempotent, well‑tested pipelines
• Deep experience with Big Query or another modern cloud data warehouse
• Experience with modern ELT tooling such as SQLMesh, dbt, dltHub, Fivetran, or Airbyte
• Experience with orchestration and CI/CD (Git Hub Actions, Airflow, or equivalent)
• Infrastructure‑as‑code experience with Terraform
• Strong data modeling skills: dimensional modeling, warehouse design, testing, and observability
• A track record of technical leadership through architecture, code reviews, and mentoring

Demonstrates expertise in building and operating production data platforms, with a strong focus on SQL, Python, and modern ELT tooling. Capable of leading technical direction, mentoring teams, and ensuring data quality and observability across cloud data infrastructures.