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.

Requirements

Core Competencies

• Experience with enterprise data modeling tools (e.g., Erwin, SQL Data Modeler) and strong expertise in dimensional modeling methodologies including Star Schema, Snowflake Schema, Fact and Dimension design, and semantic modeling.

• Bachelor’s or master’s degree in computer science, Engineering, or related field.

• 8+ years of experience in data engineering, with a strong emphasis on data governance and solution design.

• Expertise in developing scalable data architectures for enterprise reporting

• Familiarity with MLOps and AI data pipelines leveraging cloud-native services such as AWS SageMaker, Glue ML, or Databricks for feature engineering and model deployment.

• Advanced knowledge of data governance tools and frameworks, including AWS Glue Data Catalog, to support enterprise-wide lineage, metadata, and compliance practices.

• Strong understanding of cloud data platforms and services – particularly AWS (Redshift, S3, EMR, Lambda) and hybrid integrations with Azure Synapse or equivalent modern data warehouse technologies.

• Proficiency in programming and scripting languages (Python, SQL, PySpark) for building testing and optimizing scalable data solutions.

• Advanced experience developing Power BI semantic models, datasets, dashboards, reports, DAX measures, Power Query transformations, Row-Level Security, and performance optimization. Experience with Tableau is a plus.

Additional Qualifications:

• Excellent analytical and troubleshooting skills with attention to detail.

• Strong communication skills to effectively articulate technical concepts to non-technical stakeholders.

• Ability to prioritize tasks in a dynamic environment and manage multiple initiatives simultaneously.

• Certifications in cloud, database, and programming are a plus.