Top Analysis Reporting Jobs in Nashville, TN

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Leads the GTM analytics roadmap, modernizes spreadsheet reporting into scalable products, establishes trusted metrics and data models, and builds self-service and AI-assisted analytics. Partners cross-functionally with Sales, Marketing, Customer Success, Finance, Data Engineering, and Systems to improve pipeline conversion, productivity, retention, expansion, and planning. The role requires hands-on analytical product development, executive communication, data governance, and influence without formal authority.

Leads the Research and Analytics Department, managing staff, databases, web applications, consultants, and research initiatives. Conducts and oversees studies involving population, economic, land use, transportation, environmental, and infrastructure trends. Develops analyses and visualizations, presents findings to public officials and stakeholders, builds external partnerships, and ensures research programs align with regional planning and policy objectives.

Lead enterprise data engineering initiatives, including Databricks ETL/ELT development and administration, Azure data integrations, data modeling, governance, and scalable infrastructure design. Partner with operations, clinical, finance, billing, sales, IT, and external stakeholders to standardize data sources, build automated pipelines, support reporting, and communicate technical insights. The role requires strong Databricks, PySpark, Spark SQL, Azure, and healthcare data experience.

Build scalable cloud-native data and machine learning solutions using Databricks, Spark, PySpark, and SQL. Develop batch and real-time healthcare data pipelines, enterprise data models, Lakehouse architectures, semantic layers, and AI-assisted analytics workflows. Partner directly with business and clinical stakeholders to discover requirements, prototype solutions, deliver demos, drive adoption, and provide technical leadership. Ensure HIPAA-compliant security, governance, lineage, auditability, and regulatory readiness.

Leads the design, engineering, reliability, security, and evolution of the enterprise data platform. Manages data engineers and database administrators while contributing hands-on to architecture, pipelines, databases, troubleshooting, performance tuning, governance, and production support. Owns platform roadmaps, DataOps standards, monitoring, disaster recovery, capacity, vendor services, and incident resolution. Partners with architecture, analytics, security, infrastructure, data science, and business teams to deliver scalable, governed, and dependable data products.

Leads the design, development, governance, and productization of enterprise data models, pipelines, data products, and ontologies. Builds machine-learning-ready datasets, implements security and lineage controls, optimizes Databricks and Delta Lake environments, establishes data-product standards, collaborates with architects and data scientists, and mentors engineering talent. The role also defines product roadmaps, service levels, usage metrics, and continuous-improvement processes for enterprise data assets supporting AI agents, analytics, and applications.

Leads product metadata strategy, governance, taxonomy, standards, and quality across publishing products and sales channels. Designs data models, validates records, manages ONIX feeds and identifiers, improves discoverability, coordinates corrections, and integrates rights information. The role collaborates with IT, marketing, sales, editorial, distribution partners, vendors, and industry standards organizations while guiding cross-functional adoption without direct supervisory authority.

Lead the architecture and implementation of scalable GCP and Databricks data lakehouse platforms. Design batch and real-time pipelines, data models, governance, cataloging, security, and performance strategies. Act as a technical lead and trusted client advisor while mentoring engineers, guiding architecture decisions, conducting proofs of concept, and supporting hands-on troubleshooting and delivery.

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.