Senior Full Stack Engineer

Job Requirements

• Design, build, and own large-scale data pipelines, ETL/ELT workflows, and data warehouse architecture (primarily AWS and Snowflake)
• Lead dbt implementation including modeling conventions, testing frameworks, and documentation standards
• Architect and deliver high-performance Python APIs and microservices connecting the data platform to downstream applications
• Build backend services supporting both real‑time and batch access patterns
• Design and manage production data workflows across AWS, Kubernetes, Docker, and Airflow using infrastructure‑as‑code (Terraform/Cloud Formation)
• Establish and enforce data quality, governance, and coding standards across internal and external data products
• Lead AI/LLM‑enabled data access initiatives to make complex datasets more discoverable and usable
• Drive cross‑functional, multi‑team data initiatives from architecture through production delivery
• Serve as the senior technical voice on platform strategy, observability, and infrastructure decisions
• Mentor engineers and potentially carry direct management responsibilities

• 10+ years of experience in data or software engineering with a proven track record on production‑grade systems at scale
• Expert‑level SQL — complex transformations, query tuning, and performance optimization
• Expert Python skills — ETL/ELT frameworks, FastAPI or Flask, clean modular code
• Proven dbt architecture experience — advanced modeling, testing, and documentation
• Deep hands‑on experience with a cloud data warehouse (Snowflake, Big Query, or Redshift), including security and performance tuning
• Production pipeline orchestration experience using Airflow, Dagster, or equivalent
• Core engineering fundamentals: system design, Git, CI/CD, Docker, Terraform
• Solid AWS experience: S3, IAM, API integrations, security configuration
• Strong communication and technical leadership skills in ambiguous, fast‑paced environments

• Experience with biological or bioinformatics data (PCR, genomic sequencing, wastewater surveillance) or working alongside lab teams
• Snowflake production ownership: RBAC, authentication patterns, cost controls
• Observability stack experience:
  Grafana, Datadog, or similar
• Experience with data ingestion tooling:
  Airbyte, Fivetran, or custom solutions
• Data governance and cataloging exposure
• BI tool data modeling experience (Tableau, Looker, Metabase)
• Kubernetes familiarity for container orchestration
• Comfort with AI-assisted development tools (Copilot, Cursor)