BI Developer

Role Overview

This role owns data products end-to-end, from source-system ingestion and data-lake modeling, through a governed semantic layer, to the custom applications and dashboards that deliver metrics to executives and operational teams. This is a hands-on position that also spans the platform and release engineering needed to run these products in production.

Key Responsibilities

• Design, build, and operate ingestion and transformation pipelines (ELT/ETL, CDC, API and file-based sources) into an analytical data lake.
• Model data and define trustworthy, reusable metrics in a semantic layer; ensure every metric traces to the source of truth.
• Develop and maintain full-stack data applications (TypeScript/React front ends; Python/FastAPI back ends) that serve metrics directly from the semantic layer.
• Deliver executive- and operator-facing dashboards and reports, including monthly/quarterly operational reviews.
• Own the platform: containerized deployment (k3s/Helm), highly-available databases (PostgreSQL HA), CI/CD pipelines, release promotion, and post-deploy validation.
• Implement data governance: cataloging, SSO/RBAC, PII protection, and comprehensive audit logging.
• Build and integrate AI/agentic tooling against a governed, access controlled datalake.
• Provision and support cross-team data access; peer-review SQL, models, and dashboards.

Required Qualifications

Technical Skills

• Strong SQL and Python; proven ELT/ETL pipeline development.
• Data modeling and semantic-layer / metrics-layer experience.
• Full-stack development experience (modern JS/TS framework + a Python web framework).
• Relational database operations, including high-availability Postgres.
• Containerized deployment and CI/CD (Docker, Kubernetes/k3s, Helm, GitLab CI or equivalent).
• BI/dashboarding experience (Tableau or a comparable tool, plus custom-built visualization).
• Data governance fundamentals: RBAC, SSO, PII handling, cataloging.

Preferred Qualifications

• Semantic-layer specialization (governed metric layers).
• Experience enabling AI/ML or agentic use cases (MCP, LLM gateways, retrieval over governed data).
• Workflow orchestration (Temporal, Airflow, or similar).
• Experience as a technical lead/owner of a production platform and mentoring other engineers.