Top Data Engineer Jobs in Indianapolis, IN

Lead managed-data and analytics engagements to build modern data ecosystems, convert insights to strategy, manage client accounts, mentor teams, ensure project delivery and quality, and drive cloud-scale data initiatives.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms. Analyze complex problems, mentor junior team members, uphold delivery standards, and build client relationships while aligning data solutions to business needs.

Design, implement, and manage advanced data ecosystems and scalable data pipelines. Analyze complex problems, mentor junior staff, maintain high-quality deliverables, and build strong client relationships while supporting managed data and analytics programs.

Design and build data infrastructure, pipelines, integration, and transformation solutions. Use AWS, Azure Data Factory, Airflow, Hadoop, and Databricks to create data lakes/warehouses, ensure data quality and security, tune performance, and collaborate with clients to deliver actionable insights and data strategies.

Lead design and implementation of data architecture, data models, and ETL/ELT pipelines across cloud platforms. Manage data warehouses/lakes, enforce data governance and security, mentor and coach teams, partner with stakeholders, and deliver scalable, cost-effective cloud data solutions.

Design, build, and maintain ETL/data pipelines and BI solutions to deliver analytics and strategic insights for clients. Use SQL, DataStage, Oracle BI, QlikView, and Snowflake-style data models, collaborate with teams, improve data quality, and support client engagements in regulated environments.

Lead design and implementation of data architecture, build and oversee data pipelines and cloud solutions, ensure governance and security compliance, manage large projects, advise clients strategically, document data models and flows, and coach diverse teams to deliver data engineering best practices.

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.

Design, build, and maintain scalable cloud-based data pipelines and models (AWS, Snowflake). Implement ETL/ELT, data quality, automated tests, monitoring, and troubleshooting. Collaborate with product owners, data scientists, and engineers to translate business requirements into reliable data solutions and document data flows and technical specifications.

Lead design and delivery of data architectures, pipelines, and BI solutions (AWS/Redshift, DataStage, ETL, SQL Server/DB2). Coach teams, engage stakeholders, apply systems thinking, validate outcomes, and optimize performance under managed services SLAs.

Lead data engineering projects and teams to design, build, and maintain ETL/ELT pipelines, data architectures, and cloud analytics solutions. Use BI tools and cloud platforms to deliver scalable reporting, mentor staff, manage client relationships, and ensure project delivery and performance tuning.

Design, build, and support cloud data solutions on Azure: develop ETL pipelines, transformations (SQL/Python/PySpark), data models, storage patterns, quality checks, monitoring, and CI/CD; collaborate with stakeholders and document architecture and runbooks.

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.