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/CloudFormation) 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 Job Qualifications Required 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, BigQuery, 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 Preferred 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) #J-18808-Ljbffr