SUMMARY:

The Data Engineer works closely with management, vendors, and IT teams across the organization on Data Engineering & Platform Modernization projects. This role is responsible for architecting and designing scalable data solutions, modernizing data lakes and data warehouses, and developing efficient data pipelines to facilitate the migration from legacy data platforms. The Data Engineer ensures a seamless Data integration, incorporating modern cloud solutions with existing on-premises data architectures. The role also involves programming, automating, maintaining Decision Support reports and dashboards, and ensuring data quality and optimize Business Intelligence(BI) solutions to support analysis for strategic decision-making and business intelligence applications. A successful candidate must be highly motivated, skilled in multitasking, and effective in team collaboration while demonstrating strong technical expertise in Microsoft Fabric and modern data engineering practices

JOB REQUIREMENTS / QUALIFICATIONS:

Required Experience:

• Azure Data engineering or Microsoft Fabric Data engineer or Microsoft Fabric engineer certification required.
• Knowledge of healthcare data ecosystems and familiarity with relevant workflows, including data integration, interoperability, and compliance standards
• Knowledge of health care operations in relevant work environment and familiarity with departmental workflows preferred.
• Relational Database – Experience in database design, optimization, and management.
• Python – Proficiency in Python for data processing and automation.
• ETL/ELT Processes – Strong knowledge of Extract, Transform, Load techniques.
• Data Warehousing – Hands-on experience with data warehouse architecture and implementation.
• Data Modeling – Experience designing and managing data models to support business analytics.
• Data Governance & Security – Familiarity with best practices for data security and regulatory compliance.
• Legacy Database Integration & SQL Migration – Experience with modernizing legacy databases and migrating SQL-based systems to the cloud or other modern architectures.
• Version control & DevOps pipeline -Experience with Implementing version control and deployment pipeline best practices using Git and Azure DevOps.
• Proficiency in T-SQL and Familiarity with PySpark and big data processing frameworks.

Preferred Experience:

• Microsoft Fabric – Experience using Microsoft Fabric for data analytics and engineering.
• Azure Data Factory – Familiarity with data integration and pipeline automation using ADF.
• Azure SQL – Knowledge of Azure-based database management and optimization.
• Data Lakes – Understanding of data lake storage and management principles.
• Lakehouse Architecture – Experience working with hybrid data models combining structured and unstructured data storage.
• Synapse Analytics – Hands-on knowledge of data warehousing and analytics services within the Azure ecosystem.

EDUCATION & SKILLS:

• Bachelor’s degree in Computer Science, Data Engineering, or a related field.
• Strong analytical and problem-solving skills.
• Excellent verbal and written communication skills.
• Ability to work independently and in a team-oriented environment.

• Pipeline Design & Optimization – Develop, monitor, and optimize data pipelines to ensure optimal performance, efficiency and scalability.
• Machine Learning/AI – Support advanced analytics initiatives by providing clean, well-structured data for AI and ML models development and deployment.
• Jupyter Notebook – Utilize Jupyter Notebook for Data processing, analysis, and reporting.
• Cloud Platforms (Azure, AWS) – Design, Build and maintain cloud-based data infrastructure for scalability and reliability.
• Big Data Pipelines – Design and implement data pipelines for processing large-scale and distributed datasets efficiently.
• Collaboration with Business Teams – Work with data analysts and stakeholders to understand business requirements and translate them into technical solutions.
• SQL Migration & Legacy Database Integration – Plan and execute database migrations from legacy systems to modern platforms, ensuring minimal downtime and data integrity.
• Maintain, troubleshoot, and optimize production and development SQL Server databases, proactively monitoring system resources.
• Implement ETL/ELT processes for efficient data transformation and integration.
• Develop and enforce data governance and security measures.
• Ensure data warehousing and data modeling align with business needs.
• Supports a Business Intelligence (BI) platform by ensuring that data is efficiently collected, processed, and made available for reporting and analytics.
• Document data processes and provide technical support as needed.