Data Engineer / ITS Data Governance

Hartford HealthCare Corp.•Farmington, CT

About The Position

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. You 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). The role also provides 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. The role works with data architects, the Data Governance Office, interoperability, analytics, research, security, and other data-engineering teams to embed governance into the H2D2 fabric. It contributes to software engineering, data engineering, data architecture, DevSecOps, and governance standards across the team and organization.

Requirements

Bachelor’s degree in computer science, engineering, mathematics, or a related quantitative field.
At least 2 years of hands-on data software development experience with applications running on-premises and/or in cloud environments such as 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.
At least 2 years of data software development experience building data and digital platforms for batch or streaming ingestion, processing data for analytics, and storing it in various stages.
Extensive experience with multiple programming languages, such as Python, C/C++, and SQL.
At least 2 years of experience in ETL design, data modeling implementation, and maintenance.
At least 2 years of experience with specialized data architecture, such as data lake, lakehouse, data fabric, or data mesh.
Proactive and self-motivated, with a passion for continuous learning and staying current with best practices, new features, and emerging technologies.
Time and project management skills; demonstrated verbal and written expression; in-person and virtual presentation skills; and a good command and understanding of data engineering and architecture concepts.

Nice To Haves

Master’s degree in healthcare administration, computer sciences, or a related field.
At least 3 years of experience working with cloud-native development; GCP is 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.
At least 3 years of data software development experience building data and digital platforms.
Understanding of FHIR and other Health Level 7 (HL7) communication standards and clinical documentation architecture.
At least 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 data quality, security, integrity, availability, and maintainability.
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.
Engineer and support MDM and RDM capabilities, including source integration, matching and survivorship logic, hierarchy and crosswalk management, reference-code synchronization, data-standard enforcement, and distribution of trusted data to downstream consumers.
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; keep metadata and documentation synchronized with deployed solutions.
Participate in design and code reviews, promote reusable patterns and automated testing, and contribute to engineering, architecture, DevSecOps, and governance standards.
Collaborate with data architects, governance, interoperability, analytics, research, security, and other data-engineering teams.