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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manager, Data Governance - **Company:** Eliassen Group - **Location:** Irvine, CA, United States - **Experience:** Experienced - **Salary:** $166,400 - $187,200 - **Contract:** Temporary to permanent - **Skills:** Airflow, Amazon Web Services, Apache HTTP Server, Microsoft Azure, BigQuery, Profiling, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Masking, Data Warehousing, Python (Programming Language), Meta-Data Management, Microsoft SQL Server, Open Source Technology, Systems Development Life Cycle, Role-Based Access Control, Data Mesh, SQL Databases, Systems Integration, Tokenization, Unstructured Data, Workflow Management Systems, Data Classification, Sql Optimization, Great Expectations (Foster Youth College-readiness and Support Program), Grafana, Mttr, Multi-Cloud, Data Layers, Data Management, Terraform, Data Pipelines, Amazon Redshift, Databricks - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/16_028650f91 ## About the Role * 6-10 years in data engineering, data architecture, or analytics engineering, including 3 or more years focused on data governance or data platform roles. * Proven record building and implementing a governance program at scale. * Hands-on experience deploying a data catalog and integrating it with active pipelines. * Experience writing and enforcing data contracts in multi-team, multi-cloud environments. * Ability to translate regulatory requirements into platform-level technical controls. * Experience leading cross-functional initiatives with engineering, product, legal, and compliance. * Technical skills: advanced SQL, Python for pipelines and automation, Azure, Databricks; familiarity with GCP or AWS is beneficial. * Knowledge of governance, quality, lineage, and observability tools such as Cyera, Alation, Atlan, Apache Atlas, Great Expectations, dbt tests, Soda Core, Monte Carlo, OpenLineage, and Marquez. * Access control design experience including RBAC, ABAC, column masking, and row-level security. * MDM design and tooling exposure such as Informatica or Reltio. * Basic Terraform or cloud IAM for policy automation. ## Description Our client seeks a deeply technical Manager, Data Governance to design, build, and operationalize an enterprise data governance program across engineering, manufacturing, and supply chain data domains. The leader will implement governance tooling, instrument data pipelines, and enforce standards at the platform level. The role partners with Security on AI governance and architects controls for unified data governance. This is a hands-on execution role focused on SQL, Azure, Databricks, and Python., * Build the governance framework from the ground up, including ownership models, classification taxonomies, data contracts, quality SLAs, and lineage standards. * Implement and administer governance tooling such as Cyera, Alation, Atlan, Apache Atlas, or open-source equivalents. * Engineer data quality rules in Databricks and SQL platforms and integrate controls into CI/CD pipelines. * Design metadata management architecture for structured, semi-structured, and unstructured data including image, audio, and video. * Write and enforce data contracts between producers and consumers across the organization. * Partner with Data Engineering to embed governance checkpoints into ingestion, transformation, and serving layers. * Drive GDPR, CCPA, and sector-specific compliance through technical enforcement at the data layer. * Report governance health metrics including data quality scores, lineage coverage, policy compliance rates, and data incident MTTR. * Define and maintain an enterprise data catalog with full lineage, ownership, and classification metadata. * Establish and enforce naming conventions, schema standards, and model governance across domains. * Architect and oversee master data management strategy and implementation. * Lead data mesh or data product governance models as appropriate. * Deploy and administer platforms for catalog, lineage, quality, and access control. * Build automated data quality pipelines with profiling, monitoring, and alerting integrated with orchestration tools such as Airflow, Dagster, or Prefect. * Implement column-level and row-level security in data warehouses including SQL Server, Databricks, BigQuery, and Redshift. * Configure data masking, tokenization, and dynamic access policies. * Own and automate the data classification engine for PII, sensitive, and regulated data. * Instrument data observability tooling such as Monte Carlo, Acceldata, or open-source equivalents. * Translate GDPR, CCPA, SOC 2, and HIPAA requirements into enforceable technical controls. * Operationalize retention, deletion, and SAR workflows with Security and Legal. * Define and audit RBAC and ABAC models across platforms. * Run quarterly data audits and produce board-ready governance reports. * Establish and run the Data Governance Council and define roles for Data Owners, Stewards, and Consumers. * Embed governance requirements into SDLC and data product lifecycles and enable self-service within guardrails. * Act as escalation point for data quality incidents, access disputes, and compliance breaches. * Plan and deliver milestones: 1-30 days assess landscape, 30-90 days stand up tooling and taxonomy, 90-180 days ship automated quality and enforce contracts, 6-12 months enterprise rollout and auditable posture.