Overview
Skills
Job Details
We are seeking a highly skilled and motivated Senior Data Engineer to join our healthcare data engineering team. This role is focused on delivering high-quality, modern data solutions in a fast-paced, agile environment. You will work closely with engineering, analytics, data science, and product teams to build and maintain scalable data pipelines both on-prem and in the cloud.
This is a great opportunity to contribute to meaningful projects that directly impact how healthcare data is processed, interpreted, and delivered to improve patient outcomes and operational efficiency.
Must-Haves:
6+ years of experience working with SQL and relational database management systems
4+ years of experience with Cloud technologies
3+ years specifically with Microsoft SQL Server
Strong familiarity with Azure
Proficiency in programming and modifying code in languages like SQL, Python, and PySpark
Experience supporting and implementing Cloud-based and on-prem data warehousing services
Hands-on experience with:
Dimensional data modeling, schema design, and data warehousing concepts
Troubleshooting data issues and ensuring data quality
Performance tuning and applying performance improvement techniques
Strong analytical mindset with willingness to identify and implement process improvements and best practices
Ability to take full ownership in a fast-paced, collaborative, team-based support environment
Excellent oral and written communication skills
Nice to Haves / Plusses:
Experience with Databricks
Familiarity with healthcare data, especially healthcare insurance feeds such as Claims, Revenue, Rx, Medicare-specific files
Day-to-Day Responsibilities:
Collaborate with data engineers, data scientists, analysts, and product teams to design and implement scalable data pipelines
Process and transform healthcare data (e.g., claims, Rx, revenue, Medicare files) from 30+ payors into the enterprise data warehouse
Support cloud migrations and modernization of existing on-premise data infrastructure
Use SQL, Python, and PySpark to build efficient, reusable data workflows
Ensure data quality, integrity, and performance
Participate in troubleshooting, root cause analysis, and remediation of data-related issues
Continuously evaluate and adopt emerging technologies and tools to enhance data capabilities
Drive best practices in data architecture, coding standards, and development methodologies