Data Systems/Solutions Engineer

Regenstrief Institute•Indianapolis, IN

1d•Hybrid

About The Position

The Data Systems / Solutions Engineer serves as a key technical contributor within the Regenstrief Data Services team, functioning as a full-stack DataOps/MLOps engineer supporting research and analytics initiatives. This role is responsible for designing, building, and maintaining scalable, reliable data systems and pipelines that enable high-quality data ingestion, transformation, storage, and analysis. The position emphasizes the development of robust, secure, and reproducible data infrastructure that supports data science, analytics, and AI-driven research. The Engineer applies modern software engineering and data engineering practices to ensure data assets are accessible, well-governed, and aligned with clinical and research requirements. This position is a hybrid position with at least one (1) to two (2) days of onsite activity based on business needs. This position is located in downtown Indianapolis IN.

Requirements

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

Responsibilities

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.