Key skills
AirflowAWSCloudPythonSparkSQLTerraformRAIMLLLMRAGData EngineeringAnalyticsSnowflakedbtCloudFormationCI/CDPrototypingCommunication
About this role
Role Overview
- Design, build, and maintain the data products that support R&D, analytics, Lab and scientific workflows, from initial design through deployment and iterations.
- Build and maintain data pipelines for large and complex datasets, from raw inputs through derived and analysis-ready datasets.
- Apply domain knowledge in genetics and bioinformatics to design data models, schemas, and abstractions that align with real research patterns and downstream analysis needs.
- Design and enforce de-identification and privacy-preserving architectures that meet HIPAA and related regulatory requirements while remaining usable for research.
- Design scalable data models to power analytics, reporting, and downstream applications.
- Maintain high standards of data quality, accuracy, lineage, and observability across data pipelines.
- Partner closely with R&D scientists, bioinformatics teams, and software engineers to translate research needs into well-structured, reusable data assets.
- Optimize storage, retrieval, and lifecycle management for large scientific files (E.g. sequencing data, intermediate artifacts, derived datasets).
- Drive rapid prototyping efforts to support exploratory, proof-of-concepts, and early-stage initiatives, while guiding the transition to production-grade systems.
- Implement best practices for data quality, validation, lineage, observability, and reproducibility to enable a trusted 360° view.
- Collaborate with product managers and domain experts to translate requirements into technical solutions.
- Establish golden paths (templates, examples, docs) and contribute to shared data product catalogs, patterns, and best practices used by other engineers.
- Provide technical guidance and mentorship to mid-level engineers.
Requirements
- Bachelor’s or Master’s degree in computer science or bioinformatics with healthcare or biotech data domain experience preferred.
- 8+ years of experience in data engineering, designing and maintaining data pipelines and cloud data architectures (e.g, Snowflake, AWS, etc).
- Strong background in bioinformatics, genomics, or computational biology (required).
- Demonstrated experience supporting scientific R&D, Lab workflows, and research teams with production-grade data systems.
- Strong proficiency in Python, SQL, and distributed processing frameworks (Spark or equivalent).
- Experience with modern orchestration tools (Airflow, dbt, Dagster).
- Experience leveraging AI-assisted development tools (e.g., LLM copilots) to accelerate data solution development.
- Familiarity with building data products that support analytics, ML, or AI applications.
- Strong data modeling expertise (dimensional, normalized, healthcare-specific schemas).
- Experience implementing CI/CD for data pipelines and IaC (Terraform, CloudFormation).
- Demonstrated ownership of production-grade data systems and end-to-end pipeline lifecycle.
- Ability to evaluate emerging data and AI technologies and recommend scalable solutions.
- Exposure to vector databases, embeddings, semantic search, or RAG-based architectures is a plus.
- Proven ability to operate effectively in fast-paced environments, balancing speed, rigor, and compliance.
- Strong written and verbal communication skills with ability to collaborate across engineering, analytics, and business stakeholders.
- Experience working with healthcare, life sciences, or other highly regulated data, including hands-on HIPAA compliance.
Tech Stack
- Airflow
- AWS
- Cloud
- Python
- Spark
- SQL
- Terraform
Benefits
- Comprehensive medical, dental, vision, life and disability plans for employees and their dependents.
- Free testing for employees and their immediate families.
- Fertility care benefits.
- Pregnancy and baby bonding leave.
- 401k benefits.
- Commuter benefits.
- Generous employee referral program.