Top Data Management Jobs in Baton Rouge, LA

Lead enterprise data governance, quality, MDM, cleansing, validation, reconciliation, and migration efforts to support ERP implementations and large transformation programs. Develop governance frameworks, coordinate data readiness and testing, produce executive reporting and presentations, manage project plans and governance artifacts, and drive stakeholder alignment and change management.

Lead a small data engineering team partnering with Engagement Marketing to build scalable ETL/ELT pipelines, ensure data quality, define marketing metrics, integrate trusted data with ad platforms, and drive the data roadmap to enable measurement and growth.

Lead a team delivering actuarial statistical reporting, regulatory submissions, and analytics. Ensure compliance with bureau requirements, drive automated reporting and UAT for policy/claims systems (Guidewire, Solartis), partner with actuarial, technology, and vendors, and manage stakeholder/regulator interactions while mentoring staff and improving data governance and reporting processes.

Own operational health, governance, and integrity of GTM customer and prospect data across Salesforce, Marketo, Demandbase and integrated systems. Implement data quality programs, manage enrichment and integrations, troubleshoot sync failures, support segmentation and reporting, document standards, and enable scalable automation and AI workflows to support Marketing, Sales, Customer Success, and Analytics.

Lead a data engineering team building and operating NPR's data warehouse, pipelines, BI, and personalization systems. Responsibilities include people leadership, hiring, cross-functional collaboration, Agile delivery, technical design and code review, automation for developer experience, incident response, and vendor management.

Lead design and build of a cloud-based data platform; manage and mentor data engineering staff; define engineering standards, data governance, and access controls; partner with stakeholders to deliver scalable, reliable data pipelines and platform services for analytics and AI.

Lead a hands-on data engineering team to build and scale production data pipelines, datasets, and observability for alternative-data products. Drive architecture, data modeling, QA, and AI-ready dataset design while coaching engineers, partnering with product and research, and owning operational excellence across Databricks, Airflow, SQL, and PySpark.

Manage CVS Health's data partnerships and monetization strategy, source new partnerships, ensure operational success, and drive data product adoption across analytics teams.

Serve as the analytics bridge between marketing/operations and data teams: translate business questions and KPIs into clear product requirements, own delivery of treated tables, semantic layers, reports and dashboards, align stakeholders on a single source of truth, run gap analyses, document processes, and ensure delivered data products drive decision-making and adoption.

Lead enterprise data strategy and governance, build AI-ready data infrastructure and BI systems, own SharePoint platform, partner cross-functionally to ensure data quality, access, compliance, and user adoption.

Manage day-to-day execution of regional assessment builds and readiness operations in Renaissance (Illuminate) DnA, maintain data quality and compliance, coordinate vendor data collection, develop documentation and training, support regional stakeholders, and continuously improve assessment and data workflows to ensure timely, accurate reporting.

Lead and develop a team delivering enterprise data engineering, analytics, and governance. Oversee data pipelines, ETL/ELT, cloud platforms, integration services, and stakeholder engagement to enable data-informed decision-making across the university system.

Analyze medical claims (inpatient, outpatient, SNF, rehab, professional, DME) to identify utilization and root causes. Pull/integrate data, perform QA, present findings, maintain attribution methodology, coordinate stakeholders, and develop tools. Primary coding: Hive, SQL, Python.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights; manage people, mentor staff, partner with product and engineering, and build accessible data products and measurement practices.