Top Data Engineer Jobs in Omaha, NE

Lead design and implementation of scalable cloud data lakes, warehouses, and marts. Build ELT/ETL pipelines, apply Data Vault 2.0 and dimensional modeling, implement data quality, lineage, observability, and governance. Optimize Snowflake/Databricks/Synapse platforms, champion CI/CD and DataOps, mentor engineers, and enable AI/ML-ready data products through collaboration with architecture and product teams.

Build and maintain data pipelines, applications, workflows, and governance within Palantir Foundry. Apply industrial engineering and operations research methods to analyze depot, MRO, and supply chain processes, integrate ERP/MRO data into models, run capacity/throughput analyses, deliver decision‑support briefings, and support adoption of advanced analytics and digital engineering. Coordinate with stakeholders and support business development activities.

Design, build, and optimize scalable Azure data platforms and lakehouse architectures. Develop ETL/ELT pipelines (PySpark/Spark SQL), enable Power BI datasets, implement CI/CD and IaC, ensure data governance/quality with Purview, support cloud migrations and production operations while collaborating with stakeholders and agile teams.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Design, build, and operate scalable cloud data pipelines and platforms (Azure preferred). Implement DevSecOps, CI/CD, automated testing, monitoring, performance tuning, and production readiness. Mentor engineers, drive operational excellence, and translate requirements into robust technical designs. Apply healthcare domain knowledge and collaborate in agile teams to deliver secure, reliable data products.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

The Data Engineer will build and maintain data platforms, deploy pipelines, monitor transformations, and collaborate with Data Scientists and DevOps.

Design, build, and optimize scalable Azure data platforms and lakehouse architectures. Develop ETL/ELT pipelines (PySpark, ADF, Fabric), enforce data governance and quality, enable Power BI reporting, lead cloud migration and CI/CD, and provide production support and performance tuning across enterprise data systems.

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

Build and support high-performance data solutions, design and operate ETL pipelines and data warehouses, monitor production analytics tools, troubleshoot system issues, collaborate with product teams, and support TechOps for analytics infrastructure.

Lead design and hands-on delivery of a next-generation data platform and pipelines (batch, ELT, near-real-time) powering BI, experimentation, and ML. Own platform architecture, data modeling, governance, observability, and mentor engineers while modernizing legacy systems and driving AI-first data engineering practices across cross-functional teams.

Lead architecture and delivery of a cloud-native, petabyte-scale data platform: define technical roadmap, mentor engineers, build ETL/ELT pipelines, enable real-time streaming, implement Iceberg table format, optimize Snowflake/Redshift/Athena compute, and integrate observability with Splunk.

Lead end-to-end data engineering on GCP: design scalable data platforms and architectures (lake/lakehouse/warehouse), build and optimize BigQuery/Spark pipelines, ensure data quality/security, enable ML workflows with Data Science, mentor engineers, and own delivery and performance.

Design, build, and optimize scalable AWS-based data pipelines, data lakes, and warehouses. Migrate legacy systems, enforce data quality/security/governance, implement ETL/ELT and big-data solutions, adopt CI/CD and IaC, collaborate with stakeholders, and provide technical leadership and troubleshooting.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

Design, build, and maintain scalable data pipelines on AWS using EMR, Spark, and PySpark. Optimize EMR clusters and Spark jobs, implement ETL/ELT processes, ensure data quality, automate ingestion, and collaborate with data scientists and analysts. Document infrastructure, participate in code reviews, and monitor performance and costs.

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 ETL/ELT data pipelines and orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, and document processes while collaborating with cross-functional teams and supporting large-scale distributed data systems.

Build and maintain ingestion pipelines, Python services, and dbt models on AWS and Snowflake. Own operational on-call responsibilities, migrate pipelines to Airflow, contribute to CI/CD, collaborate with BI/Analytics, and mentor junior teammates.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

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.

Develop and optimize SQL-based data solutions and ETL/ELT pipelines using DBeaver. Troubleshoot data quality and performance, validate data prior to production, document transformations, and collaborate with application owners and business stakeholders. Support testing, deployments, and knowledge transfer in a healthcare data environment.

Design, build, and optimize Snowflake-based data warehouses and dbt transformation pipelines. Develop scalable ETL/ELT ingestion, tune SQL and warehouse performance, implement data quality/testing (dbt tests), and maintain CI/CD and version-controlled data workflows while collaborating with analysts, data scientists, and stakeholders.

Lead graph data engineering to design, implement, and scale Neo4j-based graph platforms. Build graph models, ingestion pipelines (batch and streaming), integrate graphs with analytics/ML, establish governance, and enable graph-driven business use cases like fraud detection and Customer 360.

Lead design, build, and operate real-time, Flink-based streaming data platforms. Develop low-latency, stateful pipelines, enforce event schemas/contracts, ensure fault tolerance and observability, mentor engineers, and partner with architecture and compliance teams to deliver resilient streaming solutions for banking use cases.