Data Engineer / ITS Data Governance

Hartford Healthcare•Farmington, CT

About The Position

Work where every moment matters. Every day, more than 40,000 Hartford HealthCare colleagues come to work with one thing in common: Pride in what we do, knowing every moment matters here. We invite you to become part of Connecticut’s most comprehensive healthcare network. The creation of the HHC System Support Office recognizes the work of a large and growing group of employees whose responsibilities are continually evolving so that we and our departments now work on behalf of the system as a whole, rather than a single member organization. With the creation of our new umbrella organization we now have our own identity with a unique payroll, benefits, performance management system, service recognition programs and other common practices across the system. Position Summary: As a Data Engineer, you will design, build, deploy, and support enterprise data solutions that capture, integrate, transform, govern, and make data available for analytics, AI/ML, generative AI, operational reporting, research, and other enterprise use cases. The Data Engineer will help architect, implement, and operate robust, scalable data infrastructure across on-premises and cloud environments, with a primary focus on Hartford HealthCare's enterprise data and digital platform (H2D2). This role will also provide dedicated engineering support to the enterprise Data Governance program by implementing data-quality controls, master data management (MDM), reference data management (RDM), metadata, lineage, and governance requirements as reusable capabilities within the H2D2 data fabric.

Requirements

Bachelor’s degree in computer science, engineering, mathematics, or related quantitative field
2+ years of hands-on experience in data software development including applications running on premise and/or cloud environments (GCP, AWS or Azure)
Experience implementing automated data-quality profiling, validation, reconciliation, monitoring, and exception-management controls.
Working knowledge of data governance concepts, including metadata management, technical lineage, data classification, stewardship, and policy enforcement.
Experience integrating governance controls into cloud data pipelines, lakehouse architectures, or enterprise data platforms.
2+ years of experience in data software development that includes building data and digital platforms for batch or streaming ingestion, processing the data for analytics, and storing the data in various stages
Extensive experience multiple programming languages (Python, C/C++, SQL etc.)
2+ Years experience in ETL design and data modeling implementation, and maintenance.
Proactive and self-motivated with a passion for continuous learning and staying updated with best practices, new features, and emerging technologies.
2+ years of Experience with specialized data architecture (e.g., data lake, lake house, data fabric, data mesh)

Nice To Haves

Master’s degree in healthcare administration, computer sciences or related field
3+ years of experience working with Cloud native development. GCP preferred.
Experience with enterprise MDM and RDM platforms, including matching, survivorship, hierarchy, crosswalk, and reference-code management.
Experience with metadata catalog, lineage, or data-quality platforms; Informatica CDGC, Cloud Data Quality, or MDM experience is preferred.
Healthcare data experience involving Epic, FHIR, HL7, OMOP, clinical terminology, or other governed clinical and research data standards.
3+ years of experience in data software development that includes building data and digital platforms
Understanding of FHIR and other Health Level 7 (HL7) communication standards and clinical documentation architecture.
3+ years of Experience with specialized data architecture

Responsibilities

Design, develop, test, deploy, and operate automated, scalable data pipelines and reusable ingestion frameworks for batch, streaming, and near-real-time workloads.
Implement incremental ingestion, transformation, orchestration, observability, error handling, and recovery patterns.
Monitor and troubleshoot pipeline performance and reliability, resolve bottlenecks, and automate controls that protect the quality, security, integrity, availability, and maintainability of HHC data solutions.
Provide data-engineering support for the enterprise Data Governance program.
Build and maintain automated data-quality rules, profiling, validation, reconciliation, exception management, and monitoring capabilities; publish measurable data-quality results; and partner with data owners and stewards to investigate root causes and remediate defects at the appropriate source or transformation layer.
Engineer and support master data management (MDM) and reference data management (RDM) capabilities, including source integration, matching and survivorship logic, hierarchy and crosswalk management, reference-code synchronization, data-standard enforcement, and distribution of trusted master and reference data to downstream consumers.
Partner with data architects, the Data Governance Office, interoperability, analytics, research, security, and other data-engineering teams to embed governance into the H2D2 fabric.
Implement metadata capture, business and technical lineage, classification, ownership and stewardship attributes, access and retention controls, and policy-as-code or reusable control patterns across Bronze/Raw, Silver/Curated, and Gold/Governed data layers.
Create and maintain technical documentation, including source-to-target mappings, data dictionaries, logical and physical models, lineage, interface specifications, data-quality rules, operating procedures, runbooks, and support documentation.
Ensure metadata and documentation remain synchronized with deployed solutions.
Contribute to software engineering, data engineering, data architecture, DevSecOps, and governance standards across the team and broader organization.
Participate in design and code reviews, promote reusable patterns and automated testing, and support a culture of accountability, continuous improvement, innovation, and responsible data use.