Top Data Engineer Jobs in Omaha, NE

Designs and executes Salesforce data migrations, including extracting, transforming, cleansing, mapping, loading, validating, and reconciling large datasets. Develops ETL workflows and Salesforce API integrations, supports testing and deployment, troubleshoots data issues, maintains performance and integrity, and documents migration processes. Collaborates with architects, developers, and stakeholders to deliver reliable data solutions.

Own the design and operation of Navanta’s data backbone, including secure bank-system ingestion, lakehouse architecture, semantic data modeling, reconciliation, quality, observability, and governance. Partner with AI/ML, security, and platform teams to deliver governed, queryable data products. Lead data contracts, lineage, PII classification, access controls, pipeline reliability, and onboarding of new bank environments.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Develop enterprise data warehouse solutions and ETL pipelines using Azure Data Factory, Databricks, Snowflake, and related technologies. Process structured and unstructured healthcare data, including HL7 and FHIR files. Build stored procedures, perform data warehouse modeling, prepare ETL specifications, and use Python, source control, and scripting tools to support data integration and engineering initiatives.

Design, develop, and maintain data pipelines, ETL processes, integrations, data infrastructure, and complex data models. Build and support data warehouses and analytics solutions using SQL, PL/SQL, Oracle, Snowflake, and cloud technologies. Collaborate with analysts, architects, developers, data scientists, and business stakeholders throughout the delivery lifecycle. Provide production support, troubleshoot issues, tune database performance, and contribute to data-driven reporting and AI tools.

Designs, builds, and operates scalable batch and streaming data pipelines, ETL/ELT workflows, cloud solutions, and secure Python REST APIs. Develops data foundations for machine learning, generative AI, embeddings, and retrieval-based applications. Ensures data quality, security, observability, lineage, and reliability while optimizing performance and cost. Leads architecture and code reviews, establishes engineering standards, mentors engineers, and collaborates with product, analytics, data science, and machine learning teams.

Develops, architects, and maintains production ETL/ELT and C#/.NET data pipelines across cloud data platforms. Responsibilities include supporting Iceberg, DynamoDB, AWS Lambda, and Trino/Starburst systems; onboarding data sources; tuning performance; monitoring data quality; resolving incidents; coordinating schemas; documenting processes; mentoring engineers; and supporting releases, deployments, architecture, and roadmap decisions.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Build and maintain SQL-based integrations connecting behavioral healthcare EHR/EMR systems to the Eleos platform. Investigate pipeline failures, data discrepancies, and quality issues to identify root causes. Develop reusable extraction, automation, and validation tooling; document data models, mappings, and business logic; and create standardized data models. Collaborate with platform engineers, product teams, and customer technical teams to establish secure access and deliver scalable, reliable integrations.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

Own and improve Life360’s Ads Data Engineering platform on Databricks. Build, operate, and optimize batch and streaming pipelines, Delta Lake tables, dbt models, semantic layers, and core metrics. Integrate advertising and business data, implement quality and governance controls, manage AWS infrastructure with Terraform, reduce compute costs, and mentor engineers. Partner with analysts, ad strategists, and sales teams to deliver trusted data products while developing AI-assisted engineering tools and workflows.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Build and maintain production data ingestion, transformation, and pipeline code for a governed federal data and AI platform. Develop data products, support data science and ML teams, monitor quality and reliability, troubleshoot issues, and contribute to Terraform infrastructure and CI/CD workflows. Apply data governance, cataloging, access controls, and compliance practices within FedRAMP Moderate, NIST 800-171, and CUI environments. Collaborate with engineers and document pipelines and processes.

Own the architecture, technical direction, reliability, and evolution of a shared cloud data platform. Lead hands-on development of streaming and batch pipelines using infrastructure-as-code, build governed analytical data products, and establish data contracts, quality, security, lineage, and observability standards. Serve as the final technical escalation point, mentor engineers, lead design and code reviews, promote responsible AI-assisted coding, respond to incidents, and communicate technical tradeoffs across stakeholders.

Lead the technical direction for audience data products and build high-volume pipelines for audience creation, partner data onboarding, identity mapping, and external data delivery. Mentor engineers, oversee design reviews and build-versus-buy decisions, and develop AI-enabled engineering tooling with appropriate guardrails. Collaborate with data science, product, engineering, and external partners.

Lead the technical direction for identity and audience data systems. Design identity models, partner mappings, high-volume production pipelines, privacy controls, data quality frameworks, monitoring, and anomaly detection. Collaborate with data science, product, legal, security, and engineering teams while mentoring engineers and leading design reviews. Develop AI-enabled engineering tools and guardrails, and ensure reliable, auditable data products at scale.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Leads enterprise data platform architecture, engineering standards, technical decisions, and development of scalable data products. Designs batch and streaming systems, governance, observability, security, and reliability practices. Partners cross-functionally, mentors engineers, drives modernization and migrations, and establishes reusable patterns for data modeling, orchestration, quality, and platform operations.

Design and maintain Snowflake-based data platforms, cloud-native data lakes, and scalable ETL/ELT pipelines for asset-backed finance analytics. Orchestrate workflows with Dagster and AWS services, ensure data reconciliation and integrity, and support migration from legacy systems. Contribute to project planning, Jira backlog management, testing, cutover planning, and cross-functional go-live readiness.

Design and maintain Snowflake data pipelines and data lake architectures for asset-based finance analytics. Develop ETL/ELT workflows, validate and reconcile financial data, migrate legacy systems, and translate quantitative finance logic into scalable architectures. Support project execution through requirements breakdown, planning, testing, cutover, and go-live readiness. Collaborate across engineering, quantitative analytics, and stakeholder teams.

Design, build, and maintain scalable batch and streaming data pipelines using Databricks, PySpark, Python, SQL, Spark, and Delta Lake. Develop transformations, orchestration workflows, data models, validation processes, and curated data products. Troubleshoot performance and quality issues, apply governance and security practices, document solutions, and collaborate with architects, data scientists, DevOps teams, analysts, and clients.

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.

Leads the migration of legacy data warehouses and pipelines to an Azure and Databricks lakehouse. Defines technical architecture, reusable Azure Data Factory and Databricks pipelines, reconciliation methods, quality gates, and performance solutions. Mentors data engineers, reviews technical work, and collaborates with product, QA, platform, and customers in a remote Agile environment.