Senior Data Quality Engineer - Cloud / CDGC - Onsite - Greater Boston - No C2C
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CategoryIT
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LocationAndover, Massachusetts
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TypeContract to hire
No C2C please!
Andover, MA - Must be able to work onsite 4 days per week!
Data Quality Engineer
The Data Quality Engineer is a technical professional responsible for engineering and operationalizing data quality across the enterprise data ecosystem. This role works closely with data engineers, business stakeholders and governance teams to translate business and technical requirements into scalable test strategies, automated validation frameworks, monitoring capabilities, and data quality controls.
Responsibilities:
Qualifications:
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#LI-MB1
Andover, MA - Must be able to work onsite 4 days per week!
Data Quality Engineer
The Data Quality Engineer is a technical professional responsible for engineering and operationalizing data quality across the enterprise data ecosystem. This role works closely with data engineers, business stakeholders and governance teams to translate business and technical requirements into scalable test strategies, automated validation frameworks, monitoring capabilities, and data quality controls.
- The engineer will lead the design and implementation of data quality and automated testing frameworks and will support the implementation and operationalization of Informatica IDMC capabilities, including Data Quality and Cloud Data Governance & Catalog (CDGC).
- The role will leverage metadata, lineage, profiling, business rules, and governance capabilities to strengthen data quality, traceability, compliance, audit readiness, and controlled data delivery.
- Success in this role requires strong analytical and engineering skills, hands-on test automation expertise, and the ability to embed quality controls throughout the data development lifecycle.
- The engineer will continuously identify opportunities to increase automation, expand test coverage, improve observability, reduce manual quality assurance activities, and responsibly apply emerging AI-assisted capabilities to improve the efficiency and effectiveness of data quality engineering.
Responsibilities:
- Design, develop, and maintain scalable data quality and automated testing frameworks for data pipelines, transformations, data models, and published data products.
- Author and execute test plans, test cases, automated validation routines, and reconciliation processes that validate data integrity across environments and throughout the data lifecycle.
- Design and implement automated data quality controls, including completeness, accuracy, validity, consistency, uniqueness, timeliness, and reconciliation checks within the approved technology stack.
- Perform impact analysis on data model, pipeline, and transformation changes and validate data integrity following deployment.
- Lead investigation and root-cause analysis of data quality defects, partnering with engineering and business teams to distinguish source-data, transformation, modeling, integration, and consumption-layer issues and drive appropriate remediation.
- Expand test coverage through functional, regression, integration, reconciliation, and data performance testing strategies appropriate to enterprise data pipelines and analytical workloads.
- Embed automated data quality and regression testing into CI/CD pipelines, establishing quality gates that identify defects before deployment and validate data integrity following release.
- Support the implementation and operationalization of Informatica Cloud Data Governance & Catalog (CDGC), leveraging metadata, lineage, business rules, and governance capabilities to strengthen data quality controls and traceability.
- Support and operationalize enterprise data standards, including definitions, naming conventions, quality rules, and stewardship processes, in partnership with Data Governance.
- Document and validate business-critical data flows and maintain traceable test evidence to support audit readiness and compliance requirements.
- Establish data quality KPIs, thresholds, monitoring, alerting, and exception-management processes for proactive detection of anomalies, errors, and unexpected changes in production data.
- Develop and drive adoption of automated regression testing, reusable validation components, and reconciliation patterns that can be applied consistently across data domains and platform layers.
- Evaluate and incorporate AI-assisted capabilities into data quality engineering practices, including test case generation, data profiling, anomaly detection, root-cause analysis, test maintenance, and validation of data transformations, while maintaining appropriate human review and governance controls.
- Identify opportunities to use AI and intelligent automation to increase test coverage, accelerate defect identification and root-cause analysis, and reduce manual quality assurance activities.
- Collaborate with stakeholders to align data quality controls with enterprise change management practices.
- Serve as a data quality advocate in development cycles and partner with Data Governance to operationalize applicable governance, security, privacy, and compliance standards.
- Work closely with Data Services, Business Intelligence, Software Engineering, Platform Engineering, and Audit teams to integrate quality practices throughout the data lifecycle.
- Participate in solution design discussions to influence quality, testability, performance, observability, and compliance standards.
- Coach analysts, engineers, and management on data quality, testing, automation, and process improvement practices.
- Work proactively and independently to address project requirements and appropriately articulate issues and challenges to reduce project delivery risk.
Qualifications:
- Advanced knowledge of data quality engineering frameworks, automated testing strategies, and reusable validation patterns.
- Hands-on experience with enterprise data quality and governance technologies; Informatica IDMC Data Quality and Cloud Data Governance & Catalog (CDGC) experience strongly preferred.
- Strong understanding of metadata management, lineage tracking, data profiling, and governance standards.
- Strong proficiency in SQL for data validation, profiling, and automation workflows. • Experience with scripting or programming languages such as Python for data validation, test automation, profiling, and quality engineering.
- Strong understanding of DevOps practices, including automated testing, CI/CD integration, and quality gates.
- Understanding of AI-assisted software and data quality engineering practices, including responsible use of generative AI, machine learning, or intelligent automation for test generation, anomaly detection, data profiling, and defect analysis.
- Understanding of cloud data architecture and modern data platform concepts.
- Strong analytical, problem-solving, requirements analysis, and documentation skills.
- Ability to collaborate across technical and business teams to operationalize quality and governance standards.
- Excellent verbal and written communication skills for interacting with technical teams and business stakeholders.
- 7+ years of progressive experience in data engineering, data quality, software/data testing, or related data platform roles.
- Experience implementing enterprise data quality and/or data governance technologies; Informatica IDMC Data Quality and CDGC experience strongly preferred.
- Direct experience designing automated testing frameworks and integrating data quality validation into DevOps/CI/CD pipelines.
- Direct experience developing test strategies, test plans, test cases, validation matrices, automated test suites, and reusable testing patterns.
- Direct experience authoring functional and/or technical requirements.
- Bachelor's degree in management information systems, computer information systems, computer science, engineering, a related field, or equivalent work experience.
- Certifications in Informatica Data Quality, Data Governance, AWS, test automation, or related technologies are a plus.
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#LI-MB1
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