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Responsibilities
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
Skills
- Data Engineering
- Solution Design
- Data Pipelines
- Data Integration
- Data Lake
- Data Warehouse
- Data Mart
- UDP framework
- Data Federation
- Data Virtualization
- Dimensional Data Modeling
- Star Schema
- Snowflake Schema
- Semantic Modeling
- Power BI
- DAX
- Power Query
- Row-Level Security (RLS)
- Data Governance
- HIPAA
- AWS Glue Data Catalog
- Data Lineage
- Metadata Management
- Auditability
- Data Stewardship
- Data Quality
- Performance Optimization
- Data Partitioning
- Indexing
- Compression
- Data Security
- Access Controls
- Encryption
- MLOps
- AI Data Pipelines
- AWS SageMaker
- AWS Glue ML
- Databricks
- Feature Engineering
- Model Deployment
- AWS
- Redshift
- S3
- EMR
- Lambda
- Azure Synapse
- Python
- SQL
- PySpark
- Tableau
Experience Level
- 8+ years of experience in data engineering
Education Level
- Bachelor’s or master’s degree in computer science, Engineering, or related field