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Senior Data Engineer
Posted 4d ago15 views1 applicant
Position Overview: Designing, developing, and maintaining scalable cloud-based data pipelines and integration solutions within healthcare industry. Build ETL/ELT processes to support enterprise analytics, working with large datasets using Python, SQL, and distributed computing technologies like Spark on AWS platform.
Key Responsibilities:
- Design, develop, and maintain scalable cloud-based data pipelines and integration solutions
- Build ETL/ELT processes that support enterprise analytics and operational reporting
- Process and transform large datasets using distributed computing technologies
- Develop reusable data engineering components that improve scalability and maintainability
- Partner with business analysts, product owners, and engineering teams to understand requirements
- Participate in solution design, architecture discussions, and technical reviews
- Troubleshoot production issues and implement long-term improvements
- Support testing, deployment, and ongoing maintenance of enterprise data solutions
- Contribute to engineering best practices and continuous improvement initiatives
- Participate in Agile ceremonies and collaborate with cross-functional teams
Primary Requirements:
- 5+ years of professional data engineering experience
- Healthcare or health insurance industry experience
- Strong Python and SQL skills with ETL/ELT pipeline development
- Experience processing large datasets using Spark, PySpark, Spark SQL
- Cloud-based data engineering solutions using AWS or equivalent platforms
- Data modeling, data integration, and data quality best practices
- Enterprise data pipeline design and implementation
- Independent work with cross-team collaboration
- Strong analytical, troubleshooting, and problem-solving skills
- Excellent verbal and written communication
- Agile software development environments experience
Desired Skills: Databricks experience, AWS services (S3, Lambda, Glue, EC2), pipeline scheduling/automation, Git, Jira/Agile workflows, UNIX/Linux scripting, AWS certification
Benefits: Medical, Dental, Vision, Health Savings Account, 401K, Behavioral Health Platform, Voluntary insurance options, Commuter Benefits
Responsibilities
- Design, develop, and maintain scalable cloud-based data pipelines and integration solutions
- Build ETL/ELT processes that support enterprise analytics and operational reporting
- Process and transform large datasets using distributed computing technologies
- Develop reusable data engineering components that improve scalability and maintainability
- Partner with business analysts, product owners, and engineering teams to understand requirements
- Participate in solution design, architecture discussions, and technical reviews
- Troubleshoot production issues and implement long-term improvements
- Support testing, deployment, and ongoing maintenance of enterprise data solutions
- Contribute to engineering best practices and continuous improvement initiatives
- Participate in Agile ceremonies and collaborate with cross-functional teams
Requirements
- 5+ years of professional data engineering experience
- Healthcare or health insurance industry experience
- Strong Python and SQL skills with ETL/ELT pipeline development
- Experience processing large datasets using Spark, PySpark, Spark SQL
- Cloud-based data engineering solutions using AWS or equivalent platforms
- Data modeling, data integration, and data quality best practices
- Enterprise data pipeline design and implementation
- Independent work with cross-team collaboration
- Strong analytical, troubleshooting, and problem-solving skills
- Excellent verbal and written communication
- Agile software development environments experience
