Data Engineer III

Responsibilities

• Works as a positive team member of a project that may consist of Business Analysts, Project Managers, Information Architects, Data Analysts, and/or Database Administrators to deliver quality applications and components within scope, on time, and within budget.
• Manages workload effectively and reports status of tasks in a timely manner.
• Forms tactical strategies to meet client goals.
• Participates in the evaluation of emerging technologies, developing proof‑of‑concepts, and promoting the usage of these emerging technologies.
• Leads small technical teams and mentors other analysts on regulation adherence.
• Follows standard operational procedures and HIPAA regulations.
• Develops strategies for managing complex data sets through maintaining data standards and metadata.
• Applies biomedical informatics technical standards, methodologies, and principles to research‑specific program needs, objectives, and outcomes.
• Integrates disparate sources of data for improved reporting efficiency and ease of use.
• Creates architecture documentation related to current and proposed informatic solutions.
• Performs other related duties as required.

• A bachelor's degree in a related field and five years of related experience, OR an equivalent combination of education, training, and experience.
• Proficiency in data pipeline orchestration (Airflow, DBT, Azure Data Factory, etc.)
• Proficiency in Python, Scala, or similar languages.
• Proficiency with and proven ability to drive adoption of Microsoft Fabric, Azure Synapse, Snowflake, or Databricks.
• Working knowledge of design for interoperability and standards for healthcare informatics system (e.g., OMOP CDM, FHIR, etc.)
• Working knowledge of code quality, CI/CD, and Dev Ops practices in data engineering.
• Working knowledge of and experience in development of AI/ML pipelines and feature engineering frameworks.
• Experience with integrating tools like ATLAS (OHDSI), ML platforms, or analytics tools for creating a data analytics dev environment for researchers.
• Experience with design and implementation of (or integration with) data governance, lineage, and cataloging solutions.
• Experience with design and implementation of HIPAA‑compliant and secure data architectures where applicable.
• Working knowledge of data quality, observability, and validation frameworks.
• Working knowledge of and experience with implementing access control and privacy controls (RBAC/ABAC, de‑identification).