- Design, build, and maintain data platforms, pipelines, and services that support research, analytics, and AI/ML workloads.
- Develop and maintain scalable data architectures using modern data warehouse/lakehouse patterns.
- Ensure data systems are reliable, performant, and designed for long-term sustainability.
- Implement and maintain ETL/ELT workflows, data validation, and quality monitoring processes.
- Implement CI/CD practices for data and ML workflows, including testing, version control, and environment promotion.
- Support reproducible analytics and ML pipelines, including experiment tracking and model lifecycle considerations.
- Apply best practices for monitoring, observability, and incident response across data systems.
- Design and maintain cloud-based data solutions using secure and scalable architectural patterns.
- Apply data governance, access control, and auditing practices consistent with HIPAA-aligned research environments.
- Ensure appropriate handling of sensitive data through de-identification, access management, and compliance controls.
- Optimize performance and cost efficiency across compute and storage resources.
- Work with clinical and research stakeholders to translate domain requirements into technical solutions.
- Support integration and use of clinical and biomedical data standards (e.g., EHR data, HL7/FHIR, OMOP).
- Produce well-documented data assets and technical specifications to support reuse and transparency.
- Collaborate with data engineers, researchers, analysts, and project managers to deliver high-quality solutions.
- Contribute to project planning, estimation, and execution.
- Serve as a technical resource to team members and stakeholders.
- Document systems, workflows, and architectural decisions clearly and consistently.
- Maintain current knowledge of emerging tools, technologies, and best practices in data engineering and AI.
- Leverage AI-assisted development tools responsibly to improve productivity and code quality.
- Participate in continuous improvement efforts across systems, processes, and workflows.
- Proficiency in modern data engineering concepts, including:
- Data warehouse and lakehouse architectures
- Dimensional modeling and data transformation patterns
- SQL and at least one general-purpose programming language (e.g., Python)
- Experience with CI/CD pipelines and automated testing for data and ML workflows
- Familiarity with data quality frameworks, lineage tracking, and observability tools
- Understanding of cloud platforms, identity and access management, and security best practices
- Knowledge of clinical and biomedical data standards and research workflows preferred
- Ability to analyze complex technical problems and implement effective solutions
- Strong troubleshooting skills across data ingestion, transformation, and delivery layers
- Ability to balance reliability, performance, and cost considerations
- Strong written and verbal communication skills
- Ability to document technical concepts clearly for both technical and non-technical audiences
- Demonstrated ability to collaborate effectively in multidisciplinary teams
- Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field required; Master’s degree preferred.
- Minimum of three (3) years of professional experience in data engineering, systems engineering, or a related technical role.
- Demonstrated experience in:
- Data platform or data pipeline development
- Cloud-based data system
- SQL and programmatic data processing
- DataOps or MLOps practices
- Works independently within established guidelines and best practices.
- Produces high-quality work with minimal supervision.
- Demonstrates sound judgment and attention to detail.
- Contributes to continuous improvement of tools, processes, and team effectiveness.
- Ability to work standard business hours with flexibility as needed.
- Ability to sit or stand for extended periods.
- Ability to operate a computer and standard office equipment.
- Ability to lift and move materials up to 20 pounds as needed.
- Ability to travel occasionally for meetings or training.
- Hybrid office and research environment.
- Fast-paced, deadline-driven setting.
- Requires collaboration with internal teams and external partners.
- Regular use of computers, communication tools, and office equipment.
Please note sponsorship and/or relocation are not available for this position.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
