Top Data Management Jobs in Dallas, TX

Leads enterprise-wide validation of analytical tools, including planning validation projects, assessing methodology quality and tool risk, researching best practices, and communicating findings through reports and presentations. Partners cross-functionally to influence business strategy, handles concurrent projects, develops solutions to business problems, and coaches junior staff. The role requires substantial data analysis, statistical or econometric modeling, large-scale data manipulation, open-source technology experience, and people management.

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.

Leads and develops a team of Data Migration Engineers responsible for moving customer data into SaaS products. Provides technical guidance on SQL, ETL, data mapping, scripts, troubleshooting, and quality control. Manages project priorities, risks, costs, escalations, cross-functional coordination, global handoffs, and weekend coverage. Partners with engineering and operations teams to resolve defects and improve migration processes, tooling, automation, and cloud-based data platforms.

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 Mod Op’s Data and Marketing Technology team, overseeing people management, marketing technology strategy, client and partner engagements, team training, and technical guidance. The role connects data engineering and analytics with marketing needs, guiding work across Salesforce, Adobe, Adobe Journey Optimizer, CRM, CDP, cloud data platforms, campaign data, customer lifecycle analysis, and marketing measurement. Responsibilities include mentoring team members, shaping team positioning, leading strategic initiatives, and supporting data modeling and advanced analytics.

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.

Leads enterprise data governance execution and manages a team responsible for data stewardship, quality, controls, privacy, records management, and remediation. Partners with business, Technology, Risk, Compliance, Audit, and Operations stakeholders to prioritize work, manage capacity, resolve data risks, maintain lifecycle documentation, monitor quality metrics, and ensure data meets business and regulatory requirements. Drives process improvement, accountability, stakeholder communication, and people development.

Leads enterprise data strategy, analytics, business intelligence, data engineering, governance, and AI-enablement initiatives. Manages data and analytics teams, defines strategic roadmaps, develops executive dashboards and KPI frameworks, guides data warehouse and lakehouse architecture, and delivers advanced analyses, forecasting, and predictive insights. Partners with executives to improve operational efficiency, customer experience, revenue, profitability, and organizational performance while establishing scalable, governed, and reliable data capabilities.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages sprint planning, releases, DevOps metrics, project delivery, and stakeholder communication. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, and ensures reliable, well-documented data solutions that support organizational reporting and analytics needs.

Leads the enterprise business intelligence and analytics strategy, manages BI and data analysts, and oversees analytics roadmaps, projects, platforms, dashboards, and vendor relationships. Translates complex data into actionable financial and business insights, supports SAP and Power BI platform upgrades, manages delivery timelines and resources, and coaches team members. The role also drives stakeholder engagement, continuous improvement, talent development, and data-driven decision-making across the organization.

Owns analytical projects across customer onboarding, transaction behavior, and platform operations. Designs dashboards, KPI frameworks, reporting workflows, and data pipelines while delivering actionable insights. Partners with Engineering, Product, Payment Operations, Compliance, and Finance; mentors colleagues; manages project scope and priorities; resolves ambiguity; and drives cross-functional alignment. Requires advanced SQL and data modeling skills, BI tool experience, analytical scripting familiarity, FinTech domain knowledge, and 4+ years of analytics experience.

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

Leads an enterprise data engineering team building scalable data platforms, pipelines, and AI-ready data products. Provides hands-on technical guidance, architecture input, process improvement, team mentoring, and people leadership. Develops semantic models and enterprise semantic layers enriched with metadata and business context. Partners with IT and business data teams to enable self-service data development while maintaining data quality, governance, and secure access.

Lead enterprise data platform architecture and client implementations centered on Databricks and lakehouse technologies. Design data models, governance, security, scalable pipelines, CI/CD, observability, and cost-management strategies. Develop and optimize batch and streaming workloads using Spark, PySpark, Scala, and SQL across cloud and hybrid databases. Mentor data engineering teams, review architectures and code, resolve complex platform issues, and collaborate with business, security, infrastructure, BI, and ML stakeholders to deliver reliable data products.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, campaign reporting, experimentation, and lifecycle analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and generate actionable insights. Builds churn, retention, segmentation, and LTV models while evaluating paid acquisition and customer growth opportunities using SQL, Python, BI tools, marketing platforms, and modern AI-enabled workflows.

Leads the design, delivery, and optimization of data architecture, pipelines, semantic layers, data quality frameworks, and Tableau BI platforms. Partners with stakeholders to manage requirements, project plans, sprint execution, monitoring, governance, and release delivery. Guides data engineers, manages team members, and supports AI-enabled analytics capabilities. Requires strong SQL, Spark, PySpark, SSIS, cloud data platform, CI/CD, Tableau infrastructure, and data engineering expertise.

Leads and grows the data engineering team while owning enterprise data architecture, ingestion, modeling, semantic layers, governance, secure APIs, and reliable platform outcomes. Partners with finance, operations, sales, marketing, and product to deliver governed, traceable data for products and AI agents. Sets the roadmap, manages vendors and costs, reviews architecture, contributes code, and delivers cross-functional data platform projects.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Leads financial reporting and data analytics by developing dashboards, reporting packages, KPI insights, and data workflows. Oversees reporting governance, data quality, warehousing practices, automation, and infrastructure improvements. Manages and mentors analysts while partnering with executives, Finance, IT, and business units to translate complex data into actionable insights. Requires advanced Power BI and SQL skills, Databricks experience, and expertise in data management, reporting, and analytics.

Build and maintain research data infrastructure connecting laboratory instruments, analysis pipelines, LIMS, repositories, databases, and cloud platforms. Develop ingestion and data transformation pipelines, manage metadata and persistent identifiers, deploy Kubernetes-based services, maintain CI/CD and infrastructure-as-code, and integrate AI/ML tools for data discovery. The role also evaluates data-management technologies, trains research staff, and produces technical documentation and procedures.

Lead and grow data engineering and analytics engineering teams while overseeing data platform modernization, BigQuery and Looker migrations, reusable dbt models, self-service capabilities, governance, reliability, and data foundations for AI and ML. Partner with technical and business stakeholders to prioritize roadmaps, manage hiring and performance, improve adoption, and protect sensitive healthcare data.

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 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.