Top Data Engineer Jobs in Omaha, NE

In this role, you'll build and scale high-throughput data pipelines, ensure data quality, and leverage AI for improved data operations. You'll also lead initiatives in a fast-paced environment focused on real estate data engineering.

Design, build, and maintain end-to-end batch and streaming data pipelines; develop ETL/ELT workflows and modular Python code; write and optimize SQL; implement analytics data models; monitor data quality, troubleshoot failures, and document pipelines; collaborate with stakeholders and mentor teammates. Remote role for candidates based in Latin America.

As a Data Engineer, you will build and optimize data pipelines, manage ETL workflows, and ensure efficient data access and quality within the infrastructure.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

The role involves building and optimizing data pipelines, modeling datasets, applying machine learning techniques, and supporting data strategies. Candidates should have experience with Python, data lakes, warehousing, and related technologies.

The Data Engineer at Anteriad will design, build, and optimize data pipelines, manage data integration processes, support analytics efforts, and collaborate with stakeholders to deliver scalable data solutions.

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 operate large-scale data pipelines and ETL processes to normalize varied partner datasets. Lead technical decisions, mentor engineers, implement testing and monitoring, manage data warehouses/lakes, and collaborate cross-functionally to deliver robust data infrastructure that supports ML and product teams.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Lead Data Engineer builds and maintains scalable data pipelines and lakehouse/warehouse platforms (Databricks) to support analytics, BI, reporting, and AI. Responsibilities include data modeling, ETL/ELT, medallion architecture, orchestration and monitoring (Airflow/Cron), performance tuning, CDC/incremental loads, data quality, and leading/mentoring a team while collaborating with stakeholders and maintaining documentation.

Design, build, and maintain ETL/ELT pipelines and data integration workflows to Snowflake using Azure Data Factory and DBT. Write optimized SQL, monitor and optimize pipeline performance, enforce data quality and governance, and provide technical leadership to junior engineers while collaborating with analysts, architects, and DevOps in cloud environments.

Lead design and delivery of cloud-native data pipelines and data warehouse models for insurance analytics using Snowflake, PySpark, and AWS. Drive migration/modernization from legacy systems, implement SQL transformations and data quality frameworks, optimize performance, and enforce engineering standards. Coordinate onshore/offshore teams, engage stakeholders, mentor junior engineers, and support project planning and delivery.

Design, build, and optimize scalable cloud data pipelines and infrastructure. Maintain and refactor SQL and ETL processes, automate data workflows, ensure data quality/security, and troubleshoot complex issues. Provide technical leadership and mentor junior engineers while collaborating with stakeholders to translate requirements.

Design, implement, and maintain data quality processes using cloud platforms (Azure/AWS/GCP). Collaborate with stakeholders to define standards, perform data profiling and cleansing, document issues and remediation, generate quality metrics, and support cross-functional teams to improve data architectures and integration.

Design, implement, and maintain PySpark-based data reconciliation solutions for financial systems. Build matching algorithms, integrate with rules engines, process large distributed datasets, identify and resolve discrepancies, and collaborate with analysts and architects to improve data quality and governance.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from SQL/NoSQL sources, data lakes, and streaming platforms. Ensure data quality, error handling, performance tuning, and collaborate with analysts, scientists, and architects to deliver scalable data solutions.

Design, implement, and maintain PySpark applications to automate large-scale financial data reconciliations. Build transformation and matching algorithms, integrate with rules engines, analyze data gaps, and collaborate with analysts and architects to ensure data quality and system resilience.

Design, build, and maintain scalable ETL/ELT pipelines into Snowflake, enforce data quality, optimize data models and query performance for Power BI reporting, produce documentation, and troubleshoot data issues while collaborating with stakeholders.

Design, develop, and maintain PySpark applications and ETL pipelines to process, transform, and integrate large-scale datasets from SQL, NoSQL, data lakes, and streaming sources. Optimize Spark job performance, implement robust error handling, and collaborate with data analysts, scientists, and architects using orchestration tools like Airflow or Luigi.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines using the Cloudera Hadoop ecosystem. Optimize Spark and platform performance, ensure data integrity and security, collaborate with data scientists and analysts, troubleshoot issues, and implement version control and CI/CD for Spark applications.

Design and develop Big Data applications using Java, Spark, and MapReduce on Cloudera/Hadoop ecosystems. Work with Hive, Impala, YARN, Kafka, and large datasets; write tests (JUnit), perform data analysis, and script in Unix/Python. Lead and manage global technology teams and follow industry best practices.

Design and implement big data solutions (Spark, Hive, Java, CDP). Analyze and consolidate disparate data sources, produce functional specifications, review data models, gather stakeholder requirements, validate implementations, support production deployments, investigate data quality and data lineage, and collaborate with technology leads to ensure data completeness and accuracy.

Design, build, test, and maintain high-performance Python data applications and backend services. Lead technical design, review code, mentor junior engineers, integrate with databases and cloud services, optimize performance, and ensure security, scalability, and reliability.

Design, develop, maintain, and optimize enterprise Tableau dashboards and BI solutions. Integrate and cleanse data from Oracle/Sybase/Big Data, tune SQL, automate Tableau extracts, manage Tableau Server on Linux, support testing, UAT, deployments, and troubleshoot data integrity and performance issues.