Top Senior Data Management Jobs in Dallas, TX

Leads data analysis strategy and manages junior analysts while mining large datasets, building and validating models, improving processes and reporting, evaluating products and marketing initiatives, and supporting data and storage system design. The role requires strong programming, business intelligence, data governance, and analytical leadership skills in a financial services environment.

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

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business insights.

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.

Lead design and implementation of scalable data infrastructure, pipelines, and transformations using cloud platforms. Build and optimize data warehouses (Snowflake, Databricks), orchestrate workflows (Airflow, Hadoop), ensure data quality and security, and coach teams to deliver analytics solutions that drive tax practice insights and client outcomes.

Lead development and execution of data-driven strategies, manage project planning and delivery, mentor and coach team members, ensure quality and ethical standards, design cloud data architectures and governance frameworks, analyze complex datasets to produce actionable insights, and drive continuous improvement using advanced analytics and modern data technologies.

Lead development and implementation of data governance strategies, manage client accounts and deliverables, supervise and mentor teams, analyze data to inform business decisions, ensure compliance with firm standards, and identify improvements in data management processes.

Analyzes AML and KYC data, builds regulatory reporting dashboards, develops KPIs and KRIs, identifies compliance risks and trends, improves processes and reporting, and advises leadership and business units. The role manages complex datasets, supports AML program health analysis, collaborates across teams, and contributes to projects involving data quality, operational efficiency, and compliance risk management.

Own and operate Toast’s enterprise data governance program, including data domain ownership, governance council coordination, policy compliance, business glossary management, metadata standards, catalog adoption, maturity assessments, and data literacy training. The role partners with business stakeholders, data stewards, catalog teams, and leadership to improve data trust, compliance, discoverability, and quality. It requires governance, metadata, SQL, data catalog, program management, and workshop facilitation experience.

Leads the enterprise data platform strategy, architecture, governance, and evolution. Manages a central data team and enables federated self-service analytics across business functions. Builds AI-ready data pipelines, RAG and Graph RAG architectures, custom data applications, observability, cost optimization, and AIOps capabilities. Requires hands-on expertise with Snowflake, dbt, modern ETL, Python, SQL, CI/CD, and Data Mesh architectures, along with 5+ years leading technical data teams.

Leads compliance risk data analytics and testing strategy, plans and executes analytical projects, evaluates analytical tools, prototypes emerging practices, and translates technical findings for legal, compliance, risk, and executive stakeholders. The role also includes cross-functional partnership, written and verbal communication, and coaching and mentoring junior staff.

Leads hands-on enterprise data engineering and architecture across Microsoft Fabric and Azure. Designs and optimizes data warehouses, pipelines, integrations, data models, BI solutions, DevOps practices, security, reliability, observability, and data quality standards. Troubleshoots complex production issues, guides AI enablement and Power BI capabilities, conducts technical reviews, and mentors engineers. The role requires advanced SQL, T-SQL, Python, PySpark, Data Vault, dimensional modeling, cloud data platform expertise, and technical leadership.

Manage GAINSCO’s enterprise data governance program, including governance councils, policies, business glossaries, data quality standards, stewardship, metadata, and master data management. Partner with IT, Security, Legal, Compliance, and business teams to improve data privacy, access, retention, audit readiness, and responsible AI practices. Facilitate cross-functional issue resolution, develop documentation and training, and support data literacy across the organization.

Leads the Data Engineering and Analytics team, managing people, technical strategy, intake prioritization, data pipelines, governed reporting, and stakeholder delivery. Oversees Python, SQL, dbt, Airflow, and BigQuery data products while guiding predictive analytics and machine-learning initiatives such as forecasting, recommendations, and segmentation. Establishes quality, reliability, governance, and development standards; mentors team members; supports debugging, process improvement, monthly reporting, invoices, and scorecards. The role also promotes responsible AI adoption and PHI-compliant data practices.

Designs, builds, tests, and supports enterprise data pipelines and Snowflake archival solutions for acquisitions, integrations, reporting, compliance, and analytics. Translates business and technical requirements into mappings, transformations, quality rules, and deployment plans. Collaborates with architecture, security, infrastructure, application, business, and vendor teams. Oversees validation, monitoring, documentation, cutover, go-live readiness, hypercare, and operational handoff while improving reusable, secure, cost-efficient data engineering practices.

Leads data governance for acquired-company integrations, embedding ownership, stewardship, quality, metadata, lineage, retention, privacy, legal holds, and compliant data use across structured and unstructured content. Establishes data inventories, quality rules, retention and disposal processes, governance controls, and readiness metrics. Partners with business, technology, legal, privacy, risk, security, and implementation teams, while producing executive reporting and supporting governance technology adoption.

Leads data engineering, business intelligence, and analytics teams; oversees data pipelines, integration infrastructure, database architecture, CI/CD practices, and enterprise data platform strategy. Partners with business and technology stakeholders to define requirements, improve data capabilities, maintain Ubuntu-based services, and deliver reporting and KPIs to leadership. Responsible for team hiring, development, mentoring, operational health, and technical execution across analytics and data integration platforms.

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.

Leads and develops a Data Platform engineering team while owning delivery of complex data initiatives. The role establishes delivery discipline, assesses technical and implementation risks, partners cross-functionally, and drives production reliability, data quality, observability, incident management, and on-call readiness. It also guides the evolution of scalable data ingestion, transformation, lakehouse, cloud architecture, governance, and reusable platform capabilities.

Leads data governance for acquired-company integrations, embedding governance, ownership, quality, metadata, lineage, retention, privacy, legal hold, and compliance requirements across structured and unstructured data. Establishes data inventories, stewardship models, quality controls, retention schedules, and approval processes; coordinates with business, technology, legal, risk, privacy, and security stakeholders. Supports migration, archival, modernization, analytics enablement, governance tooling, executive reporting, and cross-functional transformation readiness.

Designs, builds, tests, and supports enterprise data pipelines for acquired-company system integration and Snowflake archival. Translates business and technical requirements into mappings, transformations, quality controls, monitoring, and deployment plans. Collaborates with architecture, security, infrastructure, application, business, and vendor teams to deliver governed, reusable data solutions. Supports migration, cutover, validation, documentation, hypercare, and operational handoff while improving automation, performance, cost efficiency, and data observability.

Manages corporate real estate spatial data, CAD drawings, CAFM systems, space allocation models, reporting, data governance, and system administration. Supports project management, occupancy planning, and move management by maintaining accurate as-built drawings, monitoring space data, establishing global standards, coordinating system integrations, training users, and recommending process improvements. Requires expertise in CAD/CAFM, drafting, space planning, and corporate real estate data management.

Leads a team of five experienced analysts and data scientists while personally owning strategic analytics across ecommerce, health, supply chain, inventory, fulfillment, and merchandising. Partners with executives, establishes KPI and reporting standards, prioritizes the analytics roadmap, and delivers actionable business insights. The role also advances AI-native, self-service analytics capabilities, strengthens data governance and quality, and builds a high-performing analytics culture.

Leads a team of data engineers responsible for building and scaling Niche’s data platform. Oversees data pipelines, warehouse layers, platform reliability, data quality, monitoring, and engineering best practices. Manages roadmap execution, Agile rituals, cross-functional collaboration, hiring, mentoring, and career development. The role also drives process improvements, operational excellence, scalability, and delivery of secure, observable data solutions supporting analytics, reporting, content, and operational systems.