Design, build, and maintain scalable data pipelines to ingest, transform, and deliver data across diverse sources and environments
Develop and manage ETL/ELT processes within cloud-based data platforms (e.g., Snowflake, Databricks)
Own and maintain data transformation logic, codebases, and supporting documentation
Model and optimize data structures within data lakes and data warehouses to support analytics and reporting use cases
Ensure data reliability, integrity, and performance to meet client and application SLA requirements
Manage evolving data ingestion formats across Health Systems, Life Sciences, and Enterprise partner ecosystems
Drive automation and continuous improvement of data workflows to reduce manual effort and increase efficiency
Implement data quality checks, monitoring, and validation processes
Collaborate with business and technical stakeholders to gather requirements and translate them into scalable data solutions
Develop and promote best practices in data engineering, data modeling, and pipeline design
Conduct data profiling and support testing efforts in partnership with QA and DevOps teams

3–5 years of experience in Data Engineering, Data Integration, or a related field
Experience designing and building data pipelines and transformation workflows
Hands-on experience with cloud data platforms (Snowflake, Databricks) and cloud environments (Azure)
Strong experience with SQL and at least one programming language (e.g., Python, C#)
Experience with relational and non-relational data modeling, schema design, and performance optimization
Familiarity with data lake and data warehouse architecture

Data Engineer

Key skills