Data Engineer

Primary Responsibilities:

Data Pipeline Development
•Build and maintain ETL/ELT pipelines using SQL Server, SSIS, Azure Data Factory, Snowflake, Airflow, and dbt.
•Support and enhance legacy SSIS pipelines while contributing to modernization efforts.
•Ensure timely, accurate, and secure data movement across systems.
•Implement and maintain robust logging, error handling, and auditing frameworks.
•Participate in peer reviews and support reusable, framework-driven engineering practices.
Analytics & Semantic Layer (Legacy SSAS Support)
•Provide maintenance and enhancement support for SSAS Tabular and/or Multidimensional models.
•Troubleshoot processing issues, performance bottlenecks, and security role configurations.
•Ensure SSAS models continue to meet reporting and analytics needs during modernization transitions.
Reporting & Data Delivery
•Support reporting via SSRS, Power BI, and Tableau.
•Validate data accuracy and ensure outputs meet stakeholder needs.
•Collaborate with analytics teams to deliver actionable insights and promote self-service reporting.
•Assist with legacy SSRS report maintenance and performance tuning as needed.
Performance Tuning & Optimization
•Optimize performance across SQL Server, Snowflake, SSIS, ADF, dbt, Airflow, and reporting platforms.
•Tune T SQL queries, stored procedures, indexing strategies, and data models.
•Identify and remediate bottlenecks across pipelines, semantic layers, and reporting workloads.
•Partner with platform teams to improve reliability, scalability, and cost efficiency.
Data Modeling (Relational & Dimensional)
•Design and maintain relational and dimensional data models supporting analytics, reporting, and operational workflows.
•Apply best practices for star schema design, SCD handling, and semantic consistency.
•Document data structures, lineage, and business logic for cross-team clarity.
Documentation & Support
•Document pipeline logic, dependencies, and operational procedures.
•Respond to incidents and service requests through ServiceNow.
•Follow change control, governance, and compliance standards (HIPAA, ITIL).
Collaboration & Continuous Learning
•Participate in sprint ceremonies and design reviews.
•Communicate clearly across technical and non-technical audiences.
•Share knowledge through documentation and peer support.
•Embrace feedback and contribute to a positive, growth-oriented team culture.

Functions may include database architecture, engineering, design, optimization, security, and administration; as well as data modeling, big data development, Extract, Transform, and Load (ETL) development, storage engineering, data warehousing, data provisioning and other similar roles. Responsibilities may include Platform-as-a-Service and Cloud solution with a focus on data stores and associated eco systems. Duties may include management of design services, providing sizing and configuration assistance, ensuring strict data quality, and performing needs assessments. Analyzes current business practices, processes and procedures as well as identifying future business opportunities for leveraging data storage and retrieval system capabilities. Manages relationships with software and hardware vendors to understand the potential architectural impact of different vendor strategies and data acquisition. May design schemas, write SQL or other data markup scripting and helps to support development of Analytics and Applications that build on top of data. Selects, develops and evaluates personnel to ensure the efficient operation of the function.
- Generally work is self-directed and not prescribed.

- Works with less structured, more complex issues.

- Serves as a resource to others.

Qualifications - Internal

- Undergraduate degree or equivalent experience.
•10+ years Experience with Snowflake, Synapse, or cloud native data platforms
•Experience with SSAS or other legacy BI components
•Experience with CozyRoc SSIS+ or other third-party ETL/ELT components
•Experience with data APIs (ServiceNow, UKG, Kronos)
•Experience with healthcare data constructs or clinical systems
•Data platform or cloud certifications
•Exposure to AI/ML concepts, tools, or platforms (e.g., Azure Machine Learning, Python ML libraries, AI-assisted development tools)