Top Data Engineer Jobs in Indianapolis, IN

Design, build, and operate cloud-native data platforms and scalable pipelines using Snowflake and dbt. Lead orchestration, CI/CD, observability, and performance tuning. Collaborate on backend services and limited frontend work, mentor engineers, define modeling standards, and participate in multi-team architecture and system design.

Technical leader building scalable data pipelines, analytics infrastructure, and APIs. Own architecture, optimize large-scale query performance, mentor engineers, and partner with product and integration teams to deliver real-time and historical analytics.

Join Quora's data infrastructure team to build and maintain data lakes, pipelines, query engines, and dashboards. Work on scalability, cost and performance improvements, participate in planning and on-call rotations, and contribute to tooling and reliability. Mentorship and training provided for new graduates.

Design and maintain high-quality data pipelines, develop DBT models, collaborate with analytics and data science teams, and optimize data operations for scalability.

The Senior Data Engineer will design and build data pipelines and infrastructure to support data science and business intelligence solutions, utilizing AWS services and streaming technologies.

The Senior Data Engineer will design, build, and evolve data pipelines and models, collaborating across teams to solve complex business problems while ensuring data privacy and hygiene.

As a Senior Data Engineer, you will enhance the data lakehouse, optimize data workloads, perform complex transformations, and develop frameworks to improve delivery velocity, focusing on cybersecurity solutions.

Lead the design of DataOps platforms, focusing on scalable data infrastructures and automation frameworks. Oversee robust data orchestration and establish quality frameworks.

The intern will assist the Data Operations team in optimizing data workflows, developing automation scripts, and maintaining data systems while gaining hands-on experience.

Lead design and implementation of batch and streaming data pipelines, build data models, implement data quality and testing, manage orchestration and IaC, mentor engineers, and communicate technical direction and tradeoffs to stakeholders.

Ensure timely, high-quality data conversions from legacy systems to new platforms. Work with Implementation Specialists and Project Managers to execute conversion tasks, validate data integrity, troubleshoot issues, support deployments, and assist scheduling and customer communications.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

The Senior Data Engineer will design and implement complex data models, collaborate on data governance, lead data initiatives, and mentor team members.

Design and build Spark ETL pipelines on an AWS data platform, collaborate with stakeholders to deliver reliable large-scale data products, optimize and maintain data infrastructure, ensure data quality/governance, and manage platform operations and incident response.

Lead and scale the data analytics function, manage a distributed analytics team, deliver actionable insights, and oversee analytics projects.

Lead design, build, and optimization of Sezzle's large-scale data warehousing and pipelines. Own data infrastructure KPIs/SLAs, improve Redshift performance, evolve the data stack, implement ETL/ELT workflows, and partner with cross-functional teams to deliver reliable, scalable datasets.

As a Senior Data & Analytics Engineer, you'll lead data initiatives, manage a team, and ensure data quality while turning insights into actionable decisions across the company.

Design, build, and maintain production data pipelines and the unified data model using Snowflake, dbt, and orchestration tools. Optimize, test, document, and monitor ELT workflows, enforce engineering best practices, mentor teammates, and deliver trustworthy data products for analytics and business decisions.

Architect, build, and maintain scalable ELT data pipelines and analytics-ready data marts. Lead dbt modeling, ensure data quality, CI/CD, and documentation. Partner with stakeholders, mentor junior engineers, evaluate tooling (reverse ETL), and drive long-term data platform strategy.

Design, build, and maintain production data pipelines and tooling (CI/CD, testing, validation) using Snowflake, dbt, and orchestration tools. Model, transform, document, and optimize data to produce reliable curated datasets for analytics and solutions, enforce engineering best practices, and mentor teammates while ensuring scalability, observability, and data integrity.

Design and optimize BI semantic models and analytics workflows using PowerBI and Tabular Editor; build and transform large-scale datasets in BigQuery with dbt; implement CI/CD and deployment via GitHub and Azure DevOps; collaborate with stakeholders to deliver actionable visualizations and integrate Agentic AI into analytics pipelines.

Lead design, build, and support of cloud-based ETL, data warehousing, and reporting solutions. Develop/optimize ETL pipelines, data models, and queries; deploy containerized services on Kubernetes; automate workflows; mentor teammates; collaborate with architects, data scientists, and stakeholders to deliver scalable analytics and reporting products.

Design, build, and scale data pipelines and integrations for the TDX platform. Implement REST API integrations, ingest and unify customer data, optimize pipelines for performance and quality, ensure data security and governance, and collaborate with Product, Data Science, and customer teams to deliver reliable analytics-ready data.

Design and maintain ELT pipelines, build dimensional (star/snowflake) analytics models, implement data transformations and quality checks, document data lineage and governance, partner with BI users, and mentor junior engineers to deliver performant analytics for business reporting and KPIs.

Implement client revenue cycle data into company systems by creating and configuring ETL processes, building data transformations and logical/physical schemas, documenting solutions in Jira, attending client meetings, and mentoring team members to ensure post-implementation data integrity.