Top Data Management Jobs in Nashville, TN

Manages data engineering teams and leads architecture, design, and delivery across the data lifecycle. Builds scalable data infrastructure, pipelines, warehouses, and ETL processes using cloud and big-data technologies. Responsibilities include data modeling, system optimization, automation, analytics, predictive modeling support, and collaboration with business intelligence and data warehouse teams. The role also evaluates business needs, interprets trends, develops prototypes, and communicates analytical results to stakeholders.

Manages a data engineering team responsible for delivering and operating data platforms, pipelines, integrations, and data products. Oversees production reliability, security, governance, quality, cost, incident response, and platform lifecycle decisions. Leads hiring, coaching, performance management, workforce planning, stakeholder communication, and delivery prioritization. Applies engineering standards and improves automation, tooling, testing, documentation, and responsible AI-assisted engineering practices.

Manages a data engineering team responsible for delivering and operating data platforms, pipelines, integrations, and data products. Oversees reliability, security, governance, quality, cost, incident response, and production support. Leads architecture input, delivery planning, automation, and continuous improvement while hiring, coaching, and performance-managing 4–8 employees. Partners with stakeholders to prioritize work, communicate risks and tradeoffs, and deliver trusted data capabilities.

Manages a data engineering team responsible for delivering and operating data platforms, pipelines, integrations, and data products. Oversees delivery planning, production reliability, incident response, quality, governance, cost management, automation, and stakeholder communication. Hires, coaches, and performance-manages 4–8 employees while contributing to architecture, technical roadmaps, engineering standards, and responsible AI-assisted development practices.

Manages a data engineering team responsible for delivering and operating data platforms, pipelines, integrations, and data products. Ensures production reliability, security, governance, quality, observability, cost control, and incident response. Leads architecture and design participation, stakeholder communication, delivery planning, process improvement, and responsible AI-tool adoption. Hires, coaches, and performance-manages 4–8 direct reports while supporting workforce planning and resolving technical and operational risks.

Lead BI, reporting, and data governance for insurance BPS. Manage call center/business managers, build enterprise Power BI solutions, ensure data integrity, drive KPIs, partner with Ops/IT/Finance, and deliver reporting modernization and audit‑ready governance.

Leads the digital construction platform strategy for a major infrastructure project. Manages a team responsible for Procore administration, analytics, dashboards, and process automation. Translates departmental processes into standardized digital workflows, configures Procore, integrates project systems, builds Power BI reporting, and automates processes with Power Automate. Provides platform governance, user training, troubleshooting, documentation, and ongoing support while coordinating data management and adoption across project departments.

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.

Lead and grow a data engineering team to build a trusted, fast, composable, and self-serve data platform. Define architecture and roadmap for ingestion, transformation, governance, and analytics; ensure reliability, observability, and security; partner cross-functionally with Product, Analytics, Security, and Engineering; be a hands-on player-coach delivering scalable data systems using Snowflake, dbt, and self-serve analytics.

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.

Leads the design and delivery of enterprise-scale data, analytics, AI, Microsoft Fabric, Power BI, and data-driven application platforms. Owns analytics architecture, semantic modeling, data engineering, AI integration, governance, security, engineering standards, and technical roadmaps. Drives measurable business impact through KPIs and insights while leading architecture reviews, developing reusable patterns, and building analytics engineering, BI, data science, and technical leadership teams.

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.

Own the go-to-market master data management roadmap and governance model for accounts, contacts, and organizational hierarchies. Define golden record, matching, merging, survivorship, data quality, stewardship, and SLA standards across Salesforce, billing, finance, marketing, product, support, and warehouse systems. Partner with engineering, systems, finance, legal, and security teams to implement integrations, privacy controls, data products, and remediation programs. Report data health, risks, and business impact to leadership.

Leads Marketing's data architecture, metric governance, analytical modeling, predictive modeling, experimentation, and executive performance reporting. Manages three to four analysts while remaining hands-on with advanced SQL, Snowflake data validation, dimensional modeling, cross-system reconciliation, attribution, customer analytics, and AI-assisted natural-language analytics. Partners with engineering, governance, product, and marketing teams to define data products, ensure analytical accuracy, and establish scalable measurement standards.

Manages HR data, analytics, reporting, dashboards, pipelines, integrations, and automation. Supports compensation, performance management, workforce planning, HRIS maintenance, and employee listening initiatives. Builds data models and AI integrations, audits data integrity, develops actionable insights, and presents findings to HR and executive leadership. Requires strong compensation expertise, advanced Excel, HRIS and BI experience, scripting, data modeling, and handling confidential information.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, and segmentation. Partners with Marketing and Finance to establish performance goals and translate complex data into actionable recommendations. Automates reporting using analytics platforms, marketing automation tools, experimentation methods, and AI-enabled workflows while identifying opportunities to grow and retain high-value consumers.

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.

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.