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.

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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringdata pipelinescloud data architecturesPythonSQLdistributed processing frameworksdata modelingCI/CDIaCAI-assisted development tools

Soft Skills

collaborationcommunicationmentorshipproblem-solvingadaptabilityownershipattention to detailtime managementleadershipcritical thinking