Top Data Management Jobs in Dallas, TX

Leads enterprise data architecture initiatives by translating business requirements into scalable data designs, assessing impacts across systems and pipelines, modernizing legacy environments, and guiding AI/ML data solutions. Partners with data leaders and stakeholders, manages project plans, monitors implementation progress, and communicates risks and mitigation plans. Requires expertise in cloud and hybrid environments, data platforms, data engineering or architecture, and team or project management.

Leads Motorsports Data Infrastructure and Data Science teams while owning the strategy, architecture, and delivery of data platforms supporting telemetry, simulation, trackside, analytics, and AI/ML use cases. Responsibilities include managing managers, designing streaming and batch pipelines, productionizing ML models, setting engineering standards, managing Azure and Databricks capacity, protecting sensitive data, and partnering with racing and executive stakeholders. The role remains hands-on in architecture, coding, code review, incident support, and technical problem-solving.

Manages data risk for Capital One’s Risk Management organization by ensuring compliance with enterprise data standards, developing governance strategies and processes, monitoring data management activities, identifying remediation needs, and reporting gaps to leadership. The role provides guidance, influences enterprise policies, collaborates with stakeholders, supports change management, and addresses risks involving complex data systems and usages.

Manage the development, implementation, governance, and adoption of enterprise ontologies and optimized data models. Partner with Finance, Technology, Product, Machine Learning, and enterprise teams to support ERP replacement, semantic technology integration, data standardization, and data harmonization. Own the ontology roadmap, establish standards using W3C technologies, communicate strategic vision, manage and mentor teams, and advocate for semantic technologies across the organization.

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

Lead and develop data engineering leadership and teams to deliver scalable enterprise data products and platforms. Own product lifecycle, roadmap, and execution; design scalable pipelines and frameworks; enforce data quality, governance, and observability; partner with stakeholders to align data strategy to business outcomes; evaluate and implement modern data technologies; drive adoption and reliability across domains.

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 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.

Manages analytics and reporting teams supporting financial and operational decisions. Oversees reporting consistency, process improvement, automation, data interpretation, quality initiatives, and strategic executive reporting. Leads team training, collaborates with departmental leaders, and partners with account teams to deliver client recommendations. Requires healthcare operations and claims-data expertise, supervisory experience, and proficiency with Excel, Access, VBA, Crystal Reports, T-SQL, SSMS, and SSRS.

Leads a 25–30-person Data Engineering organization delivering batch and streaming pipelines, lakehouse models, master data systems, and analytics modernization across multiple business domains. Sets technical strategy, oversees architecture and data quality, manages delivery priorities and production SLAs, and partners with platform, governance, and AI/ML teams. Mentors engineering leaders, drives legacy modernization, and communicates roadmaps, value, and progress to executive stakeholders.

Leads LTK’s Data Engineering team, owning technical direction, architecture, pipelines, data products, governance, quality, reliability, and platform modernization. Manages and develops engineers, drives roadmaps and complex initiatives, partners with cross-functional stakeholders, and balances scalability, security, performance, cost, and business priorities. Establishes engineering standards, improves operational practices, manages risks and dependencies, and builds trusted, reusable data capabilities supporting analytics, reporting, products, and AI use cases.

Leads a team of 5–8 data engineers and analysts while remaining hands-on with Databricks and Azure data platforms. Owns scalable pipelines, Lakehouse architecture, data products, quality, governance, reliability, security, and cost management. Oversees hiring, coaching, performance, delivery prioritization, agile execution, production health, incident response, stakeholder communication, and cross-team collaboration. Helps shape global data engineering strategy, standards, technology adoption, and platform evolution.

Leads Fetch’s centralized Operations Analytics team, establishing standards, operating models, KPI definitions, dashboards, and reporting practices. Manages and develops Operations Data Analysts, partners with senior operational leaders, and delivers insights supporting strategy, staffing, process improvement, and decision-making. Oversees reporting in Hex, Tableau, and Grafana; improves data quality and automation with engineering; and translates ambiguous operational data into measurable recommendations and business outcomes.

Leads enterprise data architecture strategy and governance for Citi’s fraud domain. Responsibilities include developing data models, flows, standards, and remediation frameworks; architecting scalable data solutions; supporting analytics and AI use cases; partnering with senior stakeholders; and managing data architecture analysts. The role requires extensive financial services experience, fraud-domain expertise, enterprise data architecture capabilities, executive communication, and cross-functional leadership.

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.

Owns and maintains organizing data systems, voter-contact tools, and organizational databases. Ensures data integrity through quality assurance, audits, standardized processes, documentation, and permission management. Supports organizers by troubleshooting tools, managing data requests, providing training, and improving workflows. Writes SQL queries in BigQuery, develops reports and visualizations, supports integrations, and collaborates with engineering and analytics teams on data models and priorities.

Manages client, compliance, and performance reporting across healthcare programs using eligibility and claims data. Leads reporting analysts while remaining hands-on with advanced SQL, dashboards, data crosswalks, validation, reconciliation, root-cause analysis, and data-quality monitoring. Partners with data engineering and clients to resolve upstream issues, establish reporting standards, and guide the analytics roadmap.

Leads the architecture and implementation of Nokia’s scalable data platforms for photonic integrated circuit operations. Responsibilities include developing lakehouse and warehouse solutions, data models, batch and streaming pipelines, data quality and observability frameworks, CI/CD and DataOps practices, governance, security, and AI-ready data foundations. The role also leads and mentors product engineering teams, enables secure self-service analytics, and drives enterprise data excellence and innovation.

Build and lead Inovalon’s third-party data governance program, including data asset inventory, licensing controls, contractual rights management, data intake and release governance, audit readiness, compliance monitoring, and KPI reporting. Partner with Legal, Privacy, Procurement, and business leaders to manage data usage, renewals, access revocation, disposition, and downstream obligations. Develop a governance team while applying expertise in healthcare data, HIPAA, de-identification, and data licensing.

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.