Data Engineer

Key Responsibilities/ Accountabilities: Listing of key responsibilities / major activities necessary to fulfill the position’s purpose. If possible, please include the percentage of time spent on each key responsibility.

Advanced Data Engineering and Solution Design (80%)
- Architect and implement scalable data pipelines to process and integrate structured and unstructured data.
- Design end-to-end data solutions, including Data Lake, Data Warehouse, and Data Mart, to support analytics and operational systems.
- Leverage UDP framework to consolidate data pipelines across healthcare domains.
- Support the integration of new data domains through standardized ingestion and transformation frameworks.
- Collaborate with stakeholders to translate business requirements into scalable, high-performing data architectures.
- Integrate and optimize data access across distributed systems using data federation and virtualization tools
- Develop reusable data assets to support self-service analytics across programs and business domains.
- Design and maintain enterprise dimensional data models including fact tables, conformed dimensions, star schemas, snowflake schemas, and analytical data marts.
- Translate business and reporting requirements into scalable analytical data structures and semantic data models.
- Develop and maintain semantic layers, curated datasets, and business views to support enterprise reporting and analytics.
- Design, develop, and maintain Power BI semantic models, datasets, dashboards, and reports for internal and external stakeholders.
- Create and optimize DAX measures, calculated columns, KPIs, and business metrics to support operational and strategic reporting.
- Implement Power BI best practices including Row-Level Security (RLS), deployment pipelines, performance optimization, and governance standards.
- Partner with business users and subject matter experts to gather reporting requirements and deliver actionable analytics solutions.
- Ensure consistency of business definitions, metrics, and calculations across enterprise reporting and analytics platforms.

Data Governance and Compliance (10%)
- Develop and enforce data governance standards, ensuring consistency, accuracy, and compliance with regulatory frameworks (e.g, HIPAA).
- Implement data lineage, metadata management, and auditability practices using tools like AWS Glue Data Catalog.
- Establish and manage data stewardship frameworks to improve data quality and trust across the organization.

Performance Optimization and Security (10%)
- Optimize system performance by designing and implementing data partitioning, indexing, and compression strategies.
- Ensure data security through access controls, encryption, and secure design practices.