Data Engineer (Healthcare)

Sphere is a company focused on healthcare data solutions, and they are seeking a Data Engineer to join their healthcare data platform initiative. The role involves designing, building, and maintaining data pipelines to transform raw data into clean, reliable data streams for various use cases.

Responsibilities:

• Design, build, and maintain scalable data pipelines and workflows that ingest, transform, and deliver data from diverse sources (EHRs, claims, APIs, CRM systems) into analytics-ready structures
• Create order out of chaos — turning raw, fragmented data into clean, reliable, and scalable data streams
• Work hands-on with cloud-based data platforms such as Snowflake, Databricks, Azure Data Factory, AWS Redshift, or Google BigQuery
• Apply ETL/ELT principles, data warehousing, and data modeling best practices across the pipeline lifecycle
• Collaborate closely with client stakeholders and internal teams to understand data needs and deliver reliable solutions
• Tackle new and unfamiliar technical challenges as they arise — tooling flexibility matters more than any single platform
• Contribute to a culture of knowledge-sharing, mentoring counterparts and helping grow the skills needed to support and extend project deliverables
• Travel required: for the initial project kickoff, followed by quarterly visits

Requirements:

• 3–5+ years of professional experience in data engineering or a related role
• Strong SQL skills, including query optimization and debugging
• Proficiency in Python (or another programming/scripting language such as Scala or Java)
• Hands-on experience with at least one of: Snowflake, Databricks, Azure Data Factory, AWS Redshift, Google BigQuery
• Experience with dbt or similar transformation tools, and/or Apache Airflow or other orchestration frameworks
• Familiarity with ETL/ELT principles, data warehousing, and data modeling concepts
• Experience with cloud services (AWS, Azure, or GCP)
• Healthcare industry knowledge and experience (Epic, HL7, FHIR, claims data)
• Experience with CI/CD pipelines, Git, and DevOps workflows
• Familiarity with Infrastructure-as-Code tools (Terraform, CloudFormation)
• Experience with real-time/streaming data tools (Kafka, Kinesis, Pub/Sub)
• Containerization experience (Docker, Kubernetes)
• Relevant cloud or data tool certifications