Senior Data Engineer, Data Governance

Cargurus•Boston, MA

2d•Hybrid

About The Position

The Senior Data Engineer, Data Governance will be responsible for architecting, operationalizing, and optimizing the enterprise dataernance framework. This role involves the deployment and configuration of dataernance tools, implementation of automated data quality validation workflows, integration of metadata management platforms (e.g., data catalogs, lineage tools), and enforcement of data stewardship policies via role-based access controls and policy engines. You will collaborate closely with data architects, engineers, data analysts, and business teams to maintain high standards of data integrity, security, and accessibility.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Management, or a related field (or equivalent experience).
3+ years of experience in dataernance, data management, or a related field.
Proficiency in Python or similar languages widely used in the data engineeringmunity
Experience with data quality management tools (e.g., Monte Carlo, Metaplane)
Familiarity with dataernance tools (e.g., Alation, Atlan, data.world, Secoda).
Kledge of data privacy regulations (GDPR, CCPA, etc.) andpliance best practices.
Proficiency in data modeling, data lineage, and data classification and familiarity with Sflake, dbt and Looker
Experience with cloud platforms (AWS, Azure, GCP) and data integration tools (Fivetran, data pipelines).
Strong problem-solving skills and ability to collaborate with cross-functional teams.
Excellentmunication skills, with the ability to presentplex technical concepts to non-technical stakeholders.
Attention to detail and a passion for maintaining high-quality data.

Responsibilities

Data Governance Framework: Develop and implement a robust dataernance framework aligned with industry best practices andpany requirements.
Data Quality Management: Build tools and/or APIs to automate data quality monitoring and reporting, ensuring that data is accurate,plete, and consistent across theanization.
Data Stewardship: Act as a key advocate for data stewardship, working with business and technical teams to promote accountability for data quality and management.
Metadata Management: Build tools and/or APIs to automate metadata management, ensuring the accurate capture and categorization of data assets.
Compliance & Security: Ensure that dataernance policiesply with relevant laws, regulations (e.g., GDPR, CCPA), and industry standards. Promote data privacy best practices and work with legal andpliance teams.
Data Lineage: Develop and maintain data lineage models or build tools to automatically map how data flows through systems, ensuring transparency and traceability. Enhance out of the box lineage tools w necessary.
Collaboration & Training: Work closely with stakeholders across departments to establish dataernance policies, provide training on data standards, and promote awareness ofernance tools and practices.
Continuous Improvement: Continuously assess and enhance the dataernance framework to adapt to new business needs, technologies, and regulatory requirements.
Documentation & ing: Create and maintainprehensive documentation for dataernance processes, guidelines, and policies. Provide regular reports onernance activities and data health metrics to leadership.