Top Senior Data Management Jobs in Dallas, TX

As Senior Manager of Data Analysis, you will validate analytical tools, manage validation projects, assess quality and risks, and mentor junior staff.

Oversee data ingestion and transformation, manage finance data sources, ensure scalability and reliability of data platforms, and collaborate with stakeholders to align data engineering with organizational goals.

Lead the enterprise data platform strategy and architecture, driving Snowflake/dbt-based platform evolution, self-service Data Mesh and medallion models. Build AI-ready pipelines, RAG systems, and observability/cost frameworks while managing a central data team, supporting federated BI, and executing hands-on technical work.

Lead data-driven strategies and analytics at PwC, motivating teams to improve business performance and decision-making through advanced techniques and collaboration.

As a Manager in Data Governance at PwC, you will lead data governance strategies, supervise teams, manage client accounts, analyze data for decision-making, and ensure compliance with standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Senior Manager in Data and Analytics Engineering at PwC, you'll design data architecture strategies for healthcare, mentor teams, influence stakeholders, and enhance healthcare operations through data solutions.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

Lead data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

This role involves applying advanced data science and machine learning methods to healthcare data, leading teams, managing client relations, and developing analytics products for payers, while mentoring junior staff.

Lead and manage a team of data engineers to develop and maintain data pipelines and warehouses, ensuring data quality and adherence to best practices.

Lead second-line oversight of Data Quality controls: build and run a control monitoring program, manage annual oversight planning, perform design and operating effectiveness assessments, enforce sampling and evidence standards in the firm GRC tool, provide MI and challenge first-line owners, and lead a small team to drive remediation and reporting to senior management.

The Data Operations Sr. Manager leads data operations by managing daily workflows, improving data quality, and collaborating with teams to deliver data solutions. Responsibilities include troubleshooting issues, automating processes, and coaching the operations team while ensuring compliance with healthcare data standards.

Manage and develop a team of data analysts to deliver strategic analytics projects, ensure data accuracy, build reports and dashboards, translate insights into business recommendations, improve operational processes, coordinate cross-functional projects, and support budgeting and team development.

The Manager, Global Data Privacy will lead the data privacy program, influencing compliance, implementing privacy solutions, and managing a team of analysts across various business functions.

Lead and scale the enterprise data hub: manage the data warehouse, build reliable ingestion and transformation pipelines, implement governance and data modeling best practices, deliver GTM-ready metrics, enable AI/ML use cases, prioritize the data roadmap, and lead/mentor a data engineering team.

Lead a small analytics team to deliver end-to-end interactions and events analytics: define data, pull and analyze datasets, build insights and recommendations, create and automate dashboards/reports, coach team members, partner with stakeholders, and drive continuous improvement to improve client interactions, retention, and operational performance.

Manage IT teams in the design, development, testing, and implementation of applications, ensuring data integrity and project success while overseeing budgets and team performance.

This role involves leading the Data Engineering team, managing large-scale data pipeline delivery, ensuring data quality, and fostering partnerships with other teams for data governance and project management.

Lead and build an end-to-end data testing and quality assurance function, driving data governance adoption, automation and AI-enabled testing, stakeholder alignment, metrics reporting, and remediation across divisions while managing a team in Dallas.

Administer and maintain CRM and data systems (EveryAction, Salesforce, BigQuery, CARTO), respond to staff and affiliate data/tech requests, ensure data quality and de-duplication, create and deliver training materials, build network capacity, manage vendor/consultant relationships, and travel occasionally for in-person trainings and support.

As a Senior Data Steward Manager, you'll oversee data management and governance, ensure data quality, and work with cross-functional teams to meet data standards.