Homeoffice Senior Manager, Data Governance Engineering at EDB

**Candidate Note: This position is 100% remote for candidates based in India**

EnterpriseDB is seeking an experienced and visionary Senior Manager of Data Governance Engineering to lead our global data governance strategy and engineering execution. This role will focus on building and maintaining the next-generation data ecosystem—spanning transactional, analytical, and AI workloads—while ensuring robust governance, traceability, and data quality.

As a leader within EDB, you will manage and grow a high-performing data engineering team responsible for implementing scalable pipelines for structured and unstructured data, establishing enterprise-wide governance practices, and enabling trusted insights across the organization. You will be the primary driver of our metadata, data dictionary, lineage, and stewardship frameworks, leveraging modern governance and MDM tools.

At the core of this ecosystem lies EDB Postgres and our evolving lakehouse architecture, serving as the foundation for high-quality, governed, and innovation-ready data.

Your impact will be:

Data Governance Leadership

• Design, implement, and enforce data governance frameworks, policies, and procedures that ensure security, compliance, and high data quality.
• Lead efforts in metadata management, business glossaries, data dictionaries, and traceability across systems and workloads.
• Drive stewardship practices to establish data ownership, classification, and accountability.

Data Engineering & Pipelines

• Collaborate in  the development of scalable, reliable pipelines for transactional, analytical, and AI workloads, covering both structured and unstructured data.
• Ensure pipelines are governance-aware, embedding validation, quality checks, and lineage capture as part of the process.
• Partner with analytics, product, and AI teams to build pipelines that power BI dashboards, predictive modeling, and machine learning at scale.

Tools & Ecosystem Enablement

• Champion adoption of modern data governance and MDM platforms (e.g., DataHub) and integrate them with Postgres and lakehouse infrastructure.
• Evaluate and implement technologies for cataloging, lineage, and observability to ensure transparency and trust.
• Define and enforce standards for documentation, traceability, and reproducibility across the data ecosystem.

Team Leadership & Development

• Build and lead a world-class data engineering team focused on governance-aware data engineering.
• Provide technical guidance, mentorship, and career development opportunities for engineers.
• Foster a culture of accountability, innovation, and excellence in data practices.

Business & Compliance Alignment

• Partner with Compliance, Information Security, and business leaders to align governance with regulatory and strategic needs.
• Translate governance frameworks into engineering solutions that empower business outcomes, not just controls.
• Communicate effectively with executive stakeholders to report on data quality, compliance, and progress against KPIs.

What you will bring:

• 10+ years of experience in data engineering, data governance, or related roles, with at least 3–5 years in a leadership/managerial capacity.
• Proven expertise in building and maintaining ETL/ELT pipelines across structured, unstructured, and semi-structured data including orchestration and automation with tools such as Airflow and data modeling with frameworks like dbt. 
• Strong familiarity with data governance and MDM tools such as DataHub, Collibra, or Informatica.
• Hands-on experience with PostgreSQL and/or modern data lakehouse platforms (e.g., Databricks, Snowflake, or similar).
• Strong understanding of data governance principles, lineage, metadata, and data privacy regulations (e.g., GDPR, CCPA).
• Track record of hiring, developing, and leading engineering teams with a focus on delivering measurable outcomes.
• Excellent stakeholder management and communication skills across technical and business domains.

What will give you an edge:

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Science, or related field.
• Experience supporting AI/ML model pipelines and governance for AI-driven workloads.
• Knowledge of data quality frameworks and tooling for continuous monitoring and improvement.
• Demonstrated ability to balance governance rigor with business agility, enabling trusted and efficient outcomes.