Top Data Engineer Jobs in Nashville, TN

Lead and manage managed data and analytics engagements, build modern data ecosystems, convert insights into strategic opportunities, mentor teams, ensure project delivery, uphold quality standards, and drive cloud-scale data development and transformation initiatives for clients.

Design and build data infrastructure, ETL/ELT pipelines, and scalable cloud storage solutions. Develop data models and architecture, ensure governance and security compliance, collaborate with stakeholders, mentor junior team members, and maintain high-quality deliverables while building client relationships.

Design, implement, and manage advanced data ecosystems and scalable pipelines (ETL/ELT). Analyze complex problems, deliver managed data and analytics programs, maintain data governance, mentor junior staff, and build client relationships to drive data-driven insights and solutions.

Design and build data infrastructure, pipelines, integrations, and transformations using cloud and big-data tools. Maintain data lakes/warehouses, ensure data quality and security, use Databricks/AWS/Azure, Airflow and Hadoop for scalable processing, collaborate with clients to deliver analytics and support data strategy and performance tuning.

Lead design and implementation of data architecture, data models, ETL/ELT pipelines, and data warehouses/lakes. Ensure data governance, security, and pipeline reliability. Manage projects, mentor and develop team members, collaborate with stakeholders, and evaluate cloud/data technologies to meet business needs.

Design, build, and maintain data pipelines and ETL solutions; optimize SQL and data architectures; create BI reports and dashboards (Oracle BI, QlikView, Spotfire); analyze data to generate client insights; support client engagements, data quality improvements, and job scheduling in regulated environments.

Lead design and implementation of data architecture, pipelines, and cloud-based solutions. Manage large projects, ensure governance and security compliance, translate stakeholder requirements, advise clients strategically, and coach diverse teams to deliver data solutions.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms (AWS/Azure/GCP). Develop data models, ensure governance and security compliance, analyze complex problems, mentor junior staff, build client relationships, and enhance technical skills to deliver high-quality data solutions.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop and manage data lakes and warehouses, apply data architecture and dimensional modeling, validate data quality, implement security best practices, and collaborate with clients to deliver analytics and data strategies. Mentor peers and manage growing client responsibilities as a Senior Associate.

Lead design and delivery of cloud-based data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and data security. Manage projects, mentor team members, ensure governance compliance, collaborate with stakeholders, and optimize cloud infrastructure for performance, cost, and scalability.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, translate stakeholder requirements into technical designs, ensure data governance and security, develop data models and pipelines, advise clients strategically, and coach teams to deliver operational excellence.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Lead design and delivery of data engineering solutions for clients: build and maintain data pipelines and architectures, apply advanced analytics, implement BI visualizations, coach teams, manage managed-services SLAs, validate outcomes with clients, and drive solution roadmaps using cloud and ETL technologies.

Lead and deliver data engineering projects: design and build ETL/ELT pipelines and architectures, implement analytics solutions using cloud and BI tools, mentor teams, manage client relationships, ensure project execution and standards, and promote new technologies and process improvements.

As a Senior Data Engineer, you will design and build data pipelines, manage data architecture, ensure data security, and mentor team members while collaborating across teams to create impactful data products.

Design, build, and maintain batch and streaming data pipelines; ingest, transform, and optimize data for analytics and BI; ensure data quality, reliability, and performance; implement monitoring/alerting; support MLOps and analytics workloads; collaborate with data scientists and BI teams; document AI systems and RAG/Cortex-based solutions.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.