Top Data Engineer Jobs in Indianapolis, IN

Designs, builds, and optimizes scalable Databricks-based data platforms, pipelines, integrations, and reusable engineering tools. The role focuses on platform performance, reliability, cost efficiency, security, governance, automation, and cloud infrastructure. It collaborates with engineering, analytics, security, and other stakeholders on architecture, technical planning, production support, and continuous improvement. The position requires hands-on data engineering experience with Databricks, cloud services, Python, SQL, Spark, infrastructure-as-code, and CI/CD practices.

Build and operate a governed Snowflake enterprise data warehouse, including ingestion, orchestration, dbt transformations, infrastructure as code, security controls, reliability checks, and CI/CD. Partner with data owners and BI teams during migration from legacy systems, ensure compliance for sensitive financial data, support reconciliation and incident resolution, and mentor engineers. The role requires hands-on ownership of pipelines and platform components across development and production environments.

Lead the architecture and development of Penn Foster Group’s Databricks-based cloud data platform. Build scalable batch, streaming, CDC, and API pipelines using Python, SQL, Spark, and Azure; establish engineering standards, governance, observability, and CI/CD practices; develop AI-ready semantic data products; and mentor data engineers. Partner with BI, product, security, governance, and MLOps teams to support analytics, machine learning, generative AI, and self-service reporting.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.

Lead production of marine and anadromous species range maps under FWS standards. Aggregate, normalize, transform, and validate spatial data; analyze habitat intersections; coordinate review cycles with FWS and NOAA Fisheries subject matter experts; document quality assurance; and obtain government sign-off for deliverables.

Designs and leads enterprise data pipelines and integrations across Snowflake, Oracle Fusion, on-premises databases, and cloud applications. Ensures data quality, governance, security, scalability, and performance while supporting Power BI, AI/ML, and self-service analytics. Partners with business and technical stakeholders, leads migrations, mentors engineers, optimizes data platforms, and establishes engineering best practices.

Designs, builds, deploys, and maintains scalable data pipelines, data lake systems, batch and streaming processing, MLOps frameworks, data documentation tooling, and an agentic data exploration platform. The role supports customer-facing products and enables engineering teams through reliable infrastructure, observability, data discovery, and AI capabilities.

Build and operate scalable batch and streaming data pipelines, data lake infrastructure, MLOps frameworks, data documentation systems, and an AI-assisted data exploration tool. The role focuses on reliable data ingestion, large-scale processing, workflow orchestration, observability, and infrastructure supporting customer-facing products and machine learning development.

Build and maintain production data ingestion, transformation, and pipeline systems for a governed federal data and AI platform. Develop data products, infrastructure-as-code, CI/CD workflows, monitoring, quality controls, cataloging, and access controls within FedRAMP, NIST 800-171, and CUI constraints. Support data science and machine learning teams, troubleshoot reliability and performance issues, and document engineering processes. The role is fully remote and requires U.S. citizenship and the ability to obtain a T5/SSBI adjudicated clearance.

Build and maintain production data pipelines that transform robot logs and telemetry into reliable, queryable datasets. Design Spark-based ETL workflows, orchestrate processing with Airflow or similar tools, manage gold-layer datasets, and implement data quality, schema evolution, monitoring, and metadata practices. Collaborate with robotics, hardware, reliability, analytics, and ML teams on data modeling, fleet insights, debugging, and root-cause analysis. The role may also contribute to Python, Java, or C++ libraries supporting data capture and consumption.

Designs and leads production-grade batch, incremental, streaming, and real-time data pipelines across Microsoft Fabric and Azure. Builds Delta Lake medallion architectures, dimensional and Data Vault models, CDC and incremental-loading solutions, CI/CD automation, governance, security, observability, and data-quality controls. Supports migrations to Fabric, prepares governed data for AI and RAG workloads, and leads client-facing engineering workstreams, estimates, risk management, mentoring, and operational handover.

Own PushPress’s data platform, including ingestion, orchestration, cloud warehouse performance, governance, privacy, data quality, monitoring, and AI-ready data foundations. Build reliable pipelines integrating product, payment, and third-party data; establish data contracts, access controls, auditability, and semantic definitions; support real-time analytics and trustworthy reporting. Partner with Engineering, Data, and AI teams while maintaining infrastructure, incident response, documentation, and operational runbooks.

Build and own production data infrastructure for a healthcare AI platform. Responsibilities include REST API ingestion, Azure Event Hubs streaming into Databricks, Delta Lake medallion pipelines, entity resolution, semantic metrics, data-quality instrumentation, reconciliation, schema-drift handling, and troubleshooting source-to-warehouse discrepancies. The role requires strong Python and SQL skills, independent pipeline ownership, and experience with Databricks, Spark, PySpark, streaming, and healthcare-related data is a plus.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Build and guide data platform capabilities supporting marketing, finance, analytics, and data science. Develop data collection, integration, scoring, experimentation, analytics, and infrastructure solutions; contribute code, lead cross-functional technical initiatives, improve system resiliency through incident response, and establish engineering standards. Partner with business and technical stakeholders to define strategy and long-term roadmaps.

Builds business-critical dashboards, scorecards, reports, and ad hoc analyses using SQL, Python, Excel, Power BI, and Tableau. Analyzes KPIs, identifies trends and root causes, automates recurring reporting, validates data, and translates complex findings into actionable insights for stakeholders and leadership. The role collaborates with data engineers and cross-functional teams to improve reporting workflows, data pipelines, and decision-making.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.

Designs, develops, and maintains scalable cloud-based data pipelines and enterprise data solutions. The role covers data ingestion, transformation, orchestration, integration, data lakes, cloud warehouses, batch and streaming processes, performance optimization, governance, and lineage. The Senior Data Engineer collaborates with analysts, data scientists, engineers, stakeholders, contractors, and vendors while mentoring junior staff. Core tools include Databricks, dbt, Azure Data Factory, SQL, Python, and Azure.

Designs, develops, tests, troubleshoots, and deploys cloud-based data and analytics solutions using AWS, infrastructure as code, and software development practices. The role involves data integration, automation, application and data model design, system integration, code reviews, testing, troubleshooting, and stakeholder collaboration. It also provides informal leadership through mentoring, design sessions, coding sessions, and communication of complex technical concepts.

Own the architecture and operation of 9amHealth’s data platform, including Python and SQL ETL/ELT pipelines, AWS infrastructure, Redshift data models, semantic layers, governance, quality, monitoring, and reliability. Establish technical standards, optimize performance and costs, support AI/ML initiatives, build integrations, and mentor data team members. Partner with business and clinical stakeholders to deliver reliable data products and translate complex questions into actionable analytics.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Build and scale secure data lakehouse and warehouse infrastructure supporting analytics, machine learning, clinical AI performance monitoring, and self-service insights. Develop scalable data pipelines, models, orchestration, quality controls, and LLM-enabled analytics tools using heterogeneous clinical and EHR data. Collaborate with engineering, data science, clinical, product, and client success teams to establish trustworthy metrics and technical solutions.

Lead pre-sales technical engagements to design and optimize scalable cloud lakehouse data platforms (Snowflake/Databricks). Provide architecture reviews, ETL/ELT pipeline design, governance and AI-ready data foundations, support services sales, and translate technical tradeoffs into actionable recommendations for engineering and executive stakeholders.

Design, build, and operate production data pipelines using Python, Spark SQL, and Airflow. Canonicalize healthcare and customer data into trusted internal models, optimize distributed processing, enforce schemas, and implement quality checks and observability. Support customer onboarding, streaming pipelines, troubleshooting, on-call operations, and platform best practices while collaborating with product, analytics, engineering, data science, and customer teams.