Advanced Analytics Engineer Senior
New
N
Novant HealthHealthcare analytics
Source API remote eligibility restrictions: United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Minimum five years of analytics engineering, data engineering, data warehousing, analytics development, and/or business intelligence experience, required.
- Required Skills
- PythonSQLApache AirflowMicrosoft Power BIDatabricksAzure DevOpsPySpark
Requirements
- Have a bachelor’s degree in computer science or information systems; four additional relevant years beyond the minimum experience requirement may substitute.
- Have a minimum of five years of analytics engineering, data engineering, data warehousing, analytics development, and/or business intelligence experience.
- Demonstrate proficiency in SQL, Python, PySpark, Spark SQL, Delta Lake, and Databricks.
- Know dimensional modeling, including facts, dimensions, conformed dimensions, bridges, aggregates, surrogate keys, slowly changing dimensions, and data grain.
- Have experience developing curated data marts and reusable analytics assets using medallion architecture patterns.
- Build modular, maintainable transformation code for batch and incremental processing.
- Have experience with orchestration and scheduling tools such as Apache Airflow or Databricks Workflows.
- Use Azure DevOps, Git, pull requests, code review, and CI/CD across development, UAT, and production.
- Apply data quality practices including unit testing, reconciliation, referential-integrity validation, and regression testing.
- Understand Databricks Unity Catalog, data lineage, metadata, secure access patterns, and healthcare privacy and security requirements for PHI/PII.
Responsibilities
- Design, build, test, and maintain scalable data products and dimensional data models.
- Develop curated datasets, data marts, and reusable analytics solutions.
- Optimize data for Power BI and other reporting and analytics platforms.
- Collaborate with business and technical teams to deliver high-quality data solutions.
- Ensure data quality, governance, security, performance, and reliability.
- Mentor junior team members and promote data engineering best practices.
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