Top Data Engineer Jobs in Lincoln, NE

The Senior Data Engineer will design, implement, and manage data pipelines for real-time analytics and ML training datasets, ensuring efficient data flow and quality in a hybrid environment.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

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.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

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 large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

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 scalable Azure data pipelines and platforms using Azure Databricks, Synapse, Fabric, and Data Factory. Implement IaC (Bicep/ARM/Terraform), CI/CD with Azure DevOps, optimize Spark jobs, monitor platform stability, and collaborate with architects, BI developers, and clients to meet Azure best practices and business requirements.

Lead technical strategy and roadmap for an enterprise Databricks-based data platform. Drive governance, metadata, lineage, and semantic layer standards; build reusable data products; enable self-service analytics and scalable ML operations; mentor engineers and align platform investments with business outcomes.

Lead design and delivery of a cloud-based data platform: implement CDC/streaming ingestion, modeling, transformations, cross-region DB replication, observability, governance (PII, lineage, access), and enable downstream marts while mentoring engineers and driving IaC/automation-first practices.

Design, build, and maintain scalable ETL/data pipelines across distributed systems. Ensure data quality, integrity, security, and high availability. Monitor and optimize pipeline performance, perform root cause analysis, document systems, collaborate with global teams, and participate in on-call rotations to support production data systems.

Lead extraction, mapping, cleaning, transforming, and importing complex donor and fundraising data from legacy systems into Virtuous. Use SQL and dbt to design ETL transformations, collaborate on migration strategy, work with customers and internal teams, and solve complex data structure and relationship issues to align legacy data with Virtuous CRM best practices.

Design, build, deploy, and operate scalable production data pipelines and workflows (Airflow, BigQuery, dbt). Own medium-sized platform initiatives end-to-end, ensure SLA-driven freshness, quality, and cost controls, implement monitoring and CI/CD, participate in on-call incident response, optimize warehouse performance, and collaborate with analytics, data science, product, and engineering stakeholders.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Design, build, and own data infrastructure and ETL/ELT pipelines (Airflow, S3, Lambda) into a governed Snowflake warehouse. Implement data quality, IaC (Terraform/CDK), CI/CD, and analytics solutions (SQL, Tableau). Enable ML/LLM use cases like forecasting and anomaly detection and deliver automated reporting and data-driven marketing outputs.

Build and maintain scalable ETL pipelines using Python, Spark, and Airflow; collaborate with AI/ML and Product teams to deliver AI-native data products; identify and resolve ETL bottlenecks; ensure code quality through reviews and tests; own sprint deliverables and contribute to roadmap planning and major epics.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from varied sources. Work with event-driven and batch architectures, implement authentication and integration patterns, ensure observability/monitoring, optimize data flows for cost and performance, and collaborate with product, infrastructure, and engineering teams to onboard and maintain data sources at scale.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

Build and maintain AWS-based ETL pipelines (Glue/PySpark, MWAA/Airflow, Lambda) to ingest and process terabytes of financial data into an S3 data lake using Iceberg/Parquet. Implement metadata, monitoring, tests (90% coverage), materialized views (Trino/Athena), CloudFormation deployments, documentation, and support agile delivery and operations for federal clients.

Design and build end-to-end geospatial data systems: ingest and process satellite imagery into columnar/chunked stores, expose results via GraphQL/REST and FastAPI, build Next.js/React dashboards, automate with IaC and CI/CD, orchestrate pipelines with Prefect, optimize petabyte-scale I/O, and lead hiring, mentoring, and engineering best practices.

Design, build, and maintain scalable data models and pipelines using Python and SQL. Collaborate with product and SMEs to create analytics-ready healthcare datasets, maintain data engineering systems, document solutions, and iterate based on product feedback.