Top Data Engineer Jobs in Indianapolis, IN

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Design, build, and maintain scalable batch and near-real-time data pipelines using Python, PySpark, Databricks, AWS, and Delta Lake. Develop data models, ETL/ELT workflows, integrations, and data quality, lineage, reconciliation, governance, and privacy controls for sensitive profile and consent data. Collaborate with technical and business stakeholders, support CI/CD, monitor production systems, optimize performance and costs, troubleshoot incidents, and mentor junior engineers.

Builds and maintains data pipelines, ETL/ELT processes, data warehouses, BI systems, and data governance practices. Integrates structured and unstructured data, ensures data quality and security, monitors workflows, optimizes queries and data structures, and translates business requirements into technical solutions. Collaborates with analysts, engineers, data scientists, product teams, and stakeholders to provide reliable data infrastructure and actionable insights.

Build and support enterprise data ingestion, transformation, modeling, and integration solutions using Microsoft Fabric and Azure data services. Develop batch, API, file, database, and event-driven pipelines; create dimensional and curated data models; write and optimize SQL; resolve data-quality and pipeline issues; and support production workloads. Collaborate in Agile engineering processes using GitHub, Azure DevOps, CI/CD, code reviews, and reusable development standards.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Leads Azure data engineering initiatives, designing scalable data pipelines, ETL processes, data architectures, and governance solutions. Manages Azure Data Factory, Databricks, Delta Parquet, Azure Data Lake Storage, Profisee MDM, Azure Purview, SSIS, SSRS, Tableau, and SQL Server optimization. Implements real-time data integration, change data capture, CI/CD, automation, and reporting solutions while ensuring data quality, performance, reliability, and compliance. Provides technical leadership and mentors junior data engineering staff.

Designs and builds AWS data ingestion pipelines for structured and unstructured data supporting analytics and AI/GenAI use cases. Responsibilities include developing data lake ingestion frameworks, preparing AI-ready datasets through document parsing, chunking, and embedding pipelines, and creating reusable metadata-driven solutions. Requires AWS services, Python, and PySpark experience, with Bedrock, vector databases, and OpenSearch as preferred skills.

Build and maintain observability across enterprise data pipelines, including data quality checks, freshness, volume, schema monitoring, alerting, incident triage, root cause analysis, SLA management, and runbooks. Configure tools such as Monte Carlo, Great Expectations, CloudWatch, and Datadog to improve pipeline reliability and data trust.

Build and maintain scalable consumer data pipelines on Databricks using PySpark, Spark SQL, and Delta Lake. Implement medallion architecture, Unity Catalog, and Workflows. Optimize cluster performance and costs, and monitor and troubleshoot production jobs.

Build and support AWS-based ETL pipelines for regulatory and reporting data within the NURO program. Translate business reporting requirements into data models, emphasizing data lineage, auditability, accuracy, governance, and GxP-aware practices. Collaborate with business subject matter experts and support data processing using AWS Glue, Redshift, S3, Athena, SQL, and Python.

Develops and operates an AWS-based data platform supporting affiliate markets. Responsibilities include building ingestion and transformation pipelines, harmonizing multi-country data into common models, and providing production support, monitoring, and break-fix services through a shift rotation.

Build and maintain scalable Snowflake data pipelines and dbt-based ELT workflows using SQL and Python. Integrate multiple source systems, implement Medallion Architecture patterns, monitor pipeline reliability, resolve data quality issues, and support performance and cost optimization. Collaborate with analytics, product, and business teams to deliver trusted datasets while contributing to data modeling, governance, testing, documentation, lineage, code reviews, and deployment practices.

Build, maintain, and optimize data warehouses and ETL/ELT pipelines supporting ticketing, sales, marketing, fan engagement, and analytics. Integrate NFL and third-party data into Snowflake and Azure, develop modeled datasets, monitor data quality and ingestion, troubleshoot pipeline issues, and document schemas and workflows. The role also supports privacy, consent, data governance, and cybersecurity controls while collaborating with technical and business stakeholders. This is a full-time, onsite position in Indianapolis.

Design and modify data solutions using Databricks and Palantir Foundry; build data models and reports from ERP and legacy-system datasets; define and execute tests; and translate stakeholder reporting needs into technical specifications. The role requires SQL, Python, PySpark, database architecture, cloud architecture, software engineering practices, independent execution, documentation, and collaboration. It supports an Army client in a hybrid Arlington, Virginia environment and requires an active Secret clearance.

Maintains and monitors Azure Databricks data pipelines, troubleshoots data issues, collaborates with stakeholders on data requirements, documents processes, performs backups and disaster recovery drills, and supports data access and usage. The role requires extensive data engineering experience, Azure expertise, incident management knowledge, and strong problem-solving skills.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Lead the reverse-engineering and migration of legacy ETL pipelines from SAP BusinessObjects Data Services and JD Edwards into dbt and Databricks architectures. Build automated data validation, maintain SQL Server write-back mechanisms, collaborate with client stakeholders, and lead and mentor a small engineering team. The role requires hands-on expertise in SQL, Python, Spark, orchestration, cloud platforms, and legacy ERP and ETL migrations.

Designs, builds, tests, and maintains scalable data infrastructure and ETL/ELT pipelines for mission partners. Uses AWS Glue, Glue Catalog, and Step Functions to integrate, migrate, transform, and validate data. Develops data models, schemas, queries, and production-ready datasets while collaborating with analytics and business teams. The role may also support AI/ML pipelines, data governance, DevOps workflows, Databricks, and real-time streaming architectures.

Architects, builds, tests, and maintains scalable data infrastructure and ELT/ETL pipelines using AWS Glue, Glue Catalog, and Step Functions. Performs data modeling, SQL querying, data migration, schema optimization, validation, and data quality improvements. Partners with analytics and business teams to transform raw data into production-ready datasets. The role may also support AI/ML pipelines, governance, access controls, DevOps workflows, and real-time data architectures.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Designs, builds, operates, and modernizes secure ETL/ELT data pipelines for public health interoperability. Responsibilities include using NiFi, Kafka, Python, and SQL; deploying pipelines on Azure or AWS; implementing CI/CD with GitHub Actions; monitoring with Grafana; troubleshooting production systems; supporting legacy migrations; and ensuring data security, compliance, scalability, and reliability. The role collaborates with architects, integration engineers, data scientists, DevOps/SRE, and product teams.

Designs, builds, and maintains scalable AWS and on-premises data pipelines supporting genomic data science models. Responsibilities include API ingestion, storage, transformation, data modeling, workflow development, troubleshooting, documentation, infrastructure-as-code, and cross-functional collaboration. The role requires hands-on AWS pipeline development, HPC experience, container orchestration, Python and SQL expertise, CI/CD, secure access management, and production data engineering leadership in a consulting environment.