Top Senior Data Engineer Jobs in Indianapolis, IN

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Designs and maintains scalable data pipelines, models, integrations, and ETL/ELT processes using cloud data technologies. Analyzes complex data, implements quality controls and automated testing, troubleshoots issues, documents data flows, and improves engineering processes. Collaborates with product owners, business stakeholders, data scientists, engineers, and architects in an agile environment while translating business needs into reliable technical data solutions.

Designs and maintains scalable data pipelines, models, integrations, and ETL/ELT solutions using cloud data technologies. Analyzes complex data, implements quality controls and automated testing, troubleshoots data issues, documents data flows, and supports governance. Collaborates with product owners, stakeholders, architects, data scientists, and engineers in an agile environment while applying AI-assisted development responsibly.

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.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Design, build, and maintain AWS-based data pipelines supporting genomic data science models. Develop API ingestion, transformation, storage, and delivery workflows across cloud and on-premises environments. Use Python, SQL, AWS services, containers, HPC clusters, and infrastructure-as-code to create scalable solutions. Troubleshoot pipeline, performance, data quality, and modeling issues; implement documentation, testing, monitoring, and RBAC. Collaborate with technical and non-technical stakeholders in a fast-paced consulting environment.

Develops enterprise data pipelines, streaming ingestion systems, AWS data lakes, Snowflake warehouses, analytics solutions, and MarkLogic integrations. Designs data and software architectures, REST APIs, and JavaScript modules while supporting Agile software development. Requires extensive experience with AWS data services, Python, JavaScript, Java, databases, ETL, web development, data integration, and enterprise information architecture.

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Design and build maintainable data pipelines, ETL processes, and data architecture standards. Collaborate with product managers, analysts, and machine learning engineers to translate requirements into automated, scalable solutions. Apply software engineering best practices, asynchronous systems, APIs, streaming, testing, monitoring, and Python and SQL expertise. Help prioritize data initiatives based on business impact and communicate complex technical concepts to technical and non-technical stakeholders.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Designs and supports scalable data engineering solutions, pipelines, processing systems, and data platforms. Translates business requirements into technical solutions, contributes to architecture discussions, communicates with client stakeholders, and identifies opportunities to apply data and AI technologies. The role is client-facing, onsite or hybrid in Indianapolis, and supports potential engagement expansion.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Design, build, and operate scalable ETL/ELT pipelines and backend data services using Go. Integrate relational, NoSQL, blockchain, API, web, and social data sources; develop NLP pipelines for text extraction and classification; and support production APIs, dashboards, alerts, and intelligence systems. Collaborate with engineering, data science, and investigations teams on architecture, data quality, lineage, validation, monitoring, and client-facing products.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

Lead migration of financial data pipelines from Snowflake to AWS, design SOX-compliant dimensional models, build Medallion Architecture datasets, implement data quality, observability, CI/CD, and performance optimizations, and partner with stakeholders to deliver production-ready financial analytics at scale.

Owns the analytics layer between curated enterprise data and business users. Builds governed Gold-layer data products, dimensional models, Power BI semantic models, standardized metrics, and reusable data assets. Implements Unity Catalog governance, metadata, lineage, security, quality monitoring, reconciliation, and deployment controls. Partners with analysts, business owners, IT, data stewards, and AI engineers to deliver trusted, documented data for self-service analytics and AI consumption.

Design, build, and operate large-scale Hadoop-based data platforms and pipelines. Ingest, transform, and analyze structured and unstructured data supporting analytics, machine learning, and reporting. Develop reliable production systems using Spark, streaming technologies, SQL, orchestration tools, and distributed systems principles. Troubleshoot, document, and optimize data workloads for performance and cost. Preferred experience includes cloud Hadoop platforms, lakehouse formats, governance tools, Kubernetes, CI/CD, and infrastructure as code.

Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, Kafka, and GCP. Develop REST and GraphQL data services, CI/CD automation, observability, and data-quality frameworks. Integrate ML and NLP models into production workflows, including continuous retraining and real-time inference. Support structured and unstructured healthcare datasets, distributed systems, microservices, and reliable data infrastructure while collaborating with data science and MLOps teams.

Build and configure a government Databricks workspace, develop ingestion and integration pipelines from enterprise and legacy systems, and prepare data for reporting. Implement Unity Catalog governance, metadata management, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and SOPs to support long-term sustainment.

Lead the design and optimization of healthcare data pipelines integrating EHR, payer, HIE, and vendor data. Build AWS-based ETL/ELT infrastructure, reusable integration tooling, data quality monitoring, and ML enablement foundations. Ensure HIPAA-compliant handling of clinical data, including security controls, audit logging, encryption, and de-identification. Provide technical leadership, mentor engineers, influence platform architecture, and lead complex partner integrations involving FHIR, HL7v2, CCDA, and healthcare terminology standards.