Enterprise Data Governance

Job Description

Global Data Governance (Financial Services)

\n

\n

Founded in 1992, Cerberus is a global leader in alternative investing with approximately $70 billion in assets under management across complementary credit, private equity, and real estate strategies. We invest across the capital structure where our integrated investment platforms and proprietary operating capabilities create an edge to improve performance and drive long-term value. Our tenured teams have experience working collaboratively across asset classes, sectors, and geographies to seek strong risk-adjusted returns for our investors.

\n

\n

Role Summary

\n

We're seeking a hands-on Technical Data Governance Engineer to build, automate, and scale data governance across a global financial services environment on a Microsoft-first stack: Microsoft Purview, Microsoft Fabric, SQL Server, and Power BI. You will turn enterprise policy into automated, auditable controls covering cataloging, classification, lineage, and data quality for the data assets that matter most to the business and its regulators.

\n

The role centers on the data governance capabilities of Purview: the data map, catalog, classification, glossary, lineage, data quality, scans, and support for data-access governance. You will partner closely with data architecture, business data owners and stewards, analytics teams, and risk and compliance.

\n

\n

Main Objectives

\n

\n
• Operationalize data governance: Implement enterprise policies for cataloging, classification, ownership, lineage, and quality as automated, enforceable controls across Purview, Fabric/OneLake, SQL Server, and Power BI.
\n
• Coverage and classification at scale: Drive scan coverage and freshness across in-scope sources, with reliable, automated classification of sensitive and critical data.
\n
• End-to-end metadata and lineage: Establish automated technical metadata harvesting and lineage, and reconcile it with business lineage.
\n
• Data quality at scale: Stand up scalable DQ rules, profiling, monitoring, and issue-management workflows for critical data elements (CDEs) within Purview.
\n
• Catalog trust and adoption: Make the catalog the trusted source of truth, with a curated glossary, clear ownership and stewardship, quality scores, and certified data products.
\n
• Regulatory readiness: Enable data-lineage, ownership, and quality evidence for BCBS 239, GDPR/CCPA, SOX, and internal audits.
\n

\n

\n

Responsibilities

\n

\n
• Configure and administer Purview data governance: data map, catalog, glossary, classifications, data-access governance, and scans and integrations across SQL Server, Fabric/OneLake, and Power BI.
\n
• Drive and monitor scan coverage and freshness; configure and automate scans and onboarding of new sources using Purview APIs and SDKs.
\n
• Design and tune classification: custom classification rules, sensitivity and criticality tagging, and reconciliation against the business glossary.
\n
• Build and maintain technical lineage (SQL Server to Fabric Pipelines/Notebooks to Lakehouse/Delta/Parquet to Power BI/semantic models), using APIs where needed, and reconcile with business lineage.
\n
• Design and implement DQ frameworks (profiling, monitoring, and alerting) and reusable rule libraries and standards for CDEs, for data product teams to embed in their own pipelines.
\n
• Build DQ scorecards and dashboards tracking quality KPIs across domains.
\n
• Establish issue-management workflows for exceptions, including root-cause analysis and remediation SLAs.
\n
• Partner with stewards to define DQ dimensions (accuracy, completeness, timeliness, consistency) and enforce standards.
\n
• Maintain DQ metadata in Purview, aligned with the business glossary and technical lineage.
\n
• Support catalog curation and stewardship: glossary curation, steward workflows, ownership assignment, quality scores, and dataset certification; coach stewards and data-product owners.
\n
• Define governance standards, reusable rule and policy definitions, and guardrails that data product teams embed into their own pipelines; advise on how to operationalize them.
\n
• Create governance-health dashboards: coverage, scan freshness, lineage completeness, DQ defects, classification coverage, ownership completeness, and access-review status.
\n
• Interface with business units to prioritize CDEs, standards, and control mappings to regulatory requirements.
\n
• Prepare audit-ready artifacts: lineage views, control attestations, DQ remediation logs, ownership and classification evidence, and configuration-change history.
\n

\n

\n

Required Skills

\n

\n
• Microsoft Purview: data map and scans, classification, glossary, catalog, and data-access governance across SQL Server, Fabric/OneLake, and Power BI.
\n
• Microsoft Fabric or equivalent: understanding of Fabric Lakehouse (Delta/Parquet), Pipelines, Dataflows Gen2, Notebooks, and OneLake, or equivalent Synapse and Lakehouse experience, sufficient to govern these assets.
\n
• Financial services or regulated-industry context: working familiarity with at least one of BCBS 239, GDPR/CCPA, or SOX, and how data lineage, ownership, and quality support them.
\n
• Metadata and lineage engineering: technical lineage capture and reconciliation, metadata harvesting, schema versioning, change-impact analysis, and scripting against Purview APIs and SDKs.
\n
• Data quality: rule design, profiling, scorecards, monitoring and alerting, and issue-management workflows for CDEs.
\n
• Microsoft SQL Server: advanced T-SQL, performance-aware profiling, stored procedures, and metadata extraction (INFORMATION_SCHEMA / sys catalog).
\n
• Automation: Python (Fabric notebooks, governance automation) or PowerShell, able to script against Purview and Fabric APIs and SDKs.
\n

\n

\n

Preferred Skills

\n

\n
• Familiarity with source control and CI/CD concepts, enough to partner with data product teams who embed governance rules in their pipelines.
\n
• Data-catalog frameworks: DAMA-DMBOK, EDM Council DCAM, CDMC control framework.
\n
• Power BI governance: dataset and semantic-model management, tenant administration, certified datasets.
\n
• Data-product governance patterns.
\n
• ITSM integration: ServiceNow or Jira for DQ issues and governance exceptions.
\n
• Monitoring and observability: Log Analytics, Purview scan health, Fabric workspace governance metrics.
\n
• Access-governance concepts: Entra ID (Azure AD), RBAC, PIM, Key Vault.
\n
• Streaming and ML lineage: Kafka/streaming lineage, model-governance handshakes (ML lineage in Fabric or external platforms).
\n

\n

\n

Qualifications

\n

\n
• 6+ years in data engineering or governance, with a meaningful portion in financial services or another regulated industry.
\n
• Hands-on Purview and SQL governance experience; Fabric or equivalent Synapse and Lakehouse experience.
\n
• Bachelor's or Master's in CS, Data, Information Systems, or related, or equivalent practical experience.
\n
• Certifications (nice-to-have): Microsoft Certified Fabric Data Engineer (DP-700), Fabric Analytics Engineer (DP-600), CDMP (DAMA), DCAM (EDM Council), or CDMC Practitioner.
\n

\n

\n

Soft Skills and Behaviors

\n

\n
• Stakeholder partnership: translates business risk into technical controls; credible with both auditors and engineers.
\n
• Structured communicator: simplifies complex lineage and controls for executives; writes clear standards and runbooks.
\n
• Bias to automate: prefers templates and repeatable patterns over one-offs.
\n
• Pragmatic and risk-aware: balances speed, cost, and control strength.
\n
• Global collaborator: works across time zones; respectful of cultural differences.
\n
• Ownership mentality: drives outcomes end-to-end with measurable results.
\n

\n

\n

How Performance Is Measured (KPIs)

\n

\n
• Percentage of in-scope systems scanned and classified in Purview (coverage and freshness).
\n
• Classification coverage and accuracy for sensitive and critical data.
\n
• Lineage completeness from source to consumption for priority CDEs and data products.
\n
• DQ defect-rate reduction and MTTR for data incidents; percentage of CDEs under automated DQ monitoring.
\n
• Ownership and stewardship completeness, and glossary growth.
\n
• Audit and regulatory findings related to data controls: count and severity trend.
\n
• Adoption metrics: steward activity, certified datasets, catalog usage.
\n

\n

\n

Why This Role

\n

High impact, global scope, and a modern Microsoft stack with clear, measurable outcomes. You'll own the data-governance engine (catalog, classification, lineage, and data quality) and ship automated, auditable controls that make enterprise data trustworthy for the business and its regulators.

\n

\n

The base salary for this position is expected to be between $140,000.00 and $170,000.00. The base salary offered to the chosen candidate will be commensurate with a candidate’s relevant experience and other qualifications for the position, as determined by the Company in its sole discretion. In addition to base salary, this position is eligible for an annual discretionary bonus [which is often a meaningful portion of the compensation package], and a robust benefits package.