Top Remote Data Engineer Jobs in Indianapolis, IN

Design, build, and maintain operational and analytical data platform capabilities using Java Spring Batch, Python, AWS, Oracle, and Snowflake. Deliver end-to-end development, production rollout, and support; improve financial product data quality, modeling, and SQL performance; collaborate with business partners and squads.

Design, build, and maintain scalable batch and real-time data pipelines and platform components. Produce curated datasets, ensure data quality, observability, and lineage, and collaborate cross-functionally to deliver data products that support analytics, experimentation, and ML.

Build and maintain production data pipelines and models using SQLMesh, BigQuery, and Python. Improve observability, reliability, and performance; support finance and revenue pipelines; participate in on-call rotation and own features end-to-end.

Design, build, and maintain data integration and ETL solutions to implement client hospital data into Meduit platforms. Develop Python scripts, SQL queries, and data models; ensure data quality, transform and map source systems, and support data warehouse and architecture improvements during client implementations.

Design, build, and maintain scalable batch and streaming data pipelines on Databricks using PySpark, Delta Lake, Unity Catalog, and cloud platforms. Troubleshoot performance and quality issues, implement data governance, collaborate with cross-functional teams, and document pipelines and data models to deliver reliable, production-ready data products.

We are seeking a Data Engineer to design, build and optimize scalable data pipelines. Strong experience in Databricks, PySpark, and data warehouse concepts is required.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

The Principal Data Engineer will lead data architecture initiatives, design scalable data systems, and oversee data standards across teams while ensuring high-quality data infrastructure and guiding engineering teams.

Design, build, and maintain an internal data platform and pipelines. Ensure secure, governed, resilient data access for engineering and BI teams. Partner with architects, implement automations, support platform consumption, mentor users, and drive continuous improvement and compliance.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Designs and builds modern data applications, pipelines, transformations, enrichment processes, provisioning layers, and interfaces supporting clinical and operational healthcare processes. Analyzes clinical data to identify trends, collaborates with Product, Platform, and Architecture teams, mentors junior data engineers, and evaluates emerging technologies. The role emphasizes cloud computing, big data, data science, modern development frameworks, source control, documentation, and repeatable engineering practices.

Build and optimize scalable Databricks data pipelines across a medallion architecture. Develop data models, implement data quality checks and validation, integrate Azure services, and ensure data accuracy, governance, lineage, security, and performance. The role requires designing config-driven pipelines supporting CDC and event-based processing, maintaining CI/CD deployments, and working with structured and unstructured data across SQL and NoSQL systems.

Own product telemetry, maintain dbt models in Snowflake, support self-service reporting and NLP queries, drive product insights from user and retention data, and monitor Airbyte syncs to ensure data quality.

Sets technical direction and architectural coherence across Netflix’s Ads Data Engineering organization. The role guides cross-domain data system decisions, partners with engineering and business leaders, mentors data engineers, shapes platform-wide initiatives, and advances AI-native data engineering. Responsibilities span modern data foundations, ad-tech data flows, privacy and governance, platform migrations, scalable data products, and collaboration with Ads Platform Engineering, Data Science, and Product teams.

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

As a Data Engineer, you will build and maintain data pipelines, convert data into metrics, and operate the platform on AWS. Responsibilities include writing production code, processing data from APIs, and enhancing the orchestration layer using Dagster, while ensuring compliance in a HIPAA-regulated environment.

As a Data Engineer, you will build and maintain data pipelines, ensure data quality, support ML workflows, and optimize query performance using various technologies.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

Forward-deployed senior data engineer who deploys, hardens, integrates, and operates a data platform (lakehouse, streaming, governance) in DoD environments. Build pipelines across classification boundaries, produce accreditation evidence (STIG/cATO/FIPS), run day-2 operations, train mission teams, and mentor junior FDEs while feeding field feedback into product improvements.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

Design and maintain scalable ETL/ELT pipelines, analytical dashboards, and reporting tools supporting clinical, research, and operational priorities. Write production-quality Python, R, and SQL; develop data architecture; partner with data scientists to operationalize analytics; integrate AI-enabled workflow improvements; establish engineering standards; and mentor junior team members.

Owns analytics datasets and data products for Netflix’s cloud games infrastructure. Builds scalable pipelines, data models, metrics, dashboards, tooling, and self-service assets from massive telemetry data. Partners with infrastructure engineering, data engineering, and data science teams to improve quality of experience, capacity, and utilization insights. Responsibilities include telemetry and logging, visualization, trustworthy documentation, scalable data solutions, and independently prioritizing high-impact investments.

Design, develop, and maintain scalable data pipelines and models in Databricks and Microsoft Azure. Perform data ingestion, transformation, integration, migration, and modernization across enterprise data sources. Monitor and optimize pipeline performance, reliability, and data quality. Translate business requirements into technical solutions, collaborate with architects and engineers, follow security and governance standards, participate in code reviews, and document data platforms and processes.

Support building and maintaining ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions, S3, Redshift, RDS, EMR) to ingest, transform, and validate data. Write complex SQL, implement basic monitoring/logging, process API/JSON payloads, and collaborate with engineers, analysts, and stakeholders on deployment and documentation.