Data Engineer Jobs in Irving, TX

Design, develop, and maintain Python data pipelines and CI/CD deployments to build business data objects. Implement source-to-target mapping as pipeline code, write unit and integration tests, build engineering automations, and resolve complex application issues. Collaborate cross-functionally, apply GenAI tools to improve developer productivity, and work with AWS services and Snowflake to support data-driven applications.

As a Sr Data Engineer, you'll conduct feasibility analyses, develop applications, monitor processes, consult with users, and ensure compliance while serving as a mentor to junior analysts.

Design, build, and maintain production-grade ETL/ELT pipelines and data models using dbt, Spark, and cloud platforms. Manage ingestion, data quality, observability, semantic layers, and BI outputs. Collaborate with analysts and data scientists to deliver governed, high-quality data products and implement DataOps and CI/CD practices.

Design, build, and maintain large-scale data structures, ETL pipelines, and data models to support analytics and reporting. Integrate data from varied sources, collaborate with Data Science to operationalize models, optimize Hadoop/Spark workflows, and ensure data quality and accessibility for internal customers.

Design, build and maintain large-scale data structures, ETL pipelines, and data marts. Integrate data sources, ensure data quality, support analytics and Data Science teams, and optimize Hadoop/cloud-based processing using tools like Spark, Airflow, and Kafka.

Design, build, and manage large-scale data structures, pipelines and ETL workflows. Develop real-time and batch analytic systems, integrate diverse data sources, ensure data quality and accessibility, and collaborate with Data Science to operationalize models using Hadoop, GCP, Python, Spark, Airflow, and related tools.

Design, build, and maintain large-scale data structures, ETL pipelines, and data architectures to support analytics and reporting. Integrate data sources, ensure data quality and accessibility, collaborate with Data Science to operationalize models, and troubleshoot/optimize systems using Hadoop, cloud platforms, and modern data tooling.

Build and maintain large-scale data structures, ETL pipelines, and data models to support analytics and reporting. Use Google Cloud services (BigQuery, Cloud SQL, Cloud Storage) and programming in Python/Java/R to integrate, standardize, and ensure data quality. Collaborate with Data Science to operationalize models, optimize queries, troubleshoot systems, and recommend tooling for analytics modernization and platform capabilities.

Design, build, and maintain large-scale data structures, ETL pipelines, and data models; integrate ML models with production workflows; ensure data quality and accessibility; optimize queries and troubleshoot systems to support analytics and reporting.

Design, build, and maintain large-scale data structures, pipelines and ETL workflows to support analytics and reporting. Collaborate with Data Science to integrate models, optimize Hadoop/HDFS processing, and ensure data quality. Troubleshoot, test, and recommend tools to improve real-time and offline data processing for internal customers.

Design, build, and manage large-scale data structures and ETL pipelines on cloud platforms (GCP/AWS/Azure). Develop data marts, integrate data for analytics and data science, write production application code, collaborate with data science teams, ensure data quality, and recommend/implement tools and architecture improvements.

Design, build, and maintain large-scale data structures, ETL pipelines, and data marts to support analytics and reporting. Optimize Hadoop/Spark-based workflows, integrate cloud sources, ensure data quality, and collaborate with Data Science to operationalize models. Provide production support, monitoring, and tooling recommendations.

Build and maintain large-scale data structures, pipelines, and ETL workflows; design databases and data marts; integrate data science models into automated processes; optimize Hadoop/Spark-based systems; develop real-time and offline analytic tooling; ensure data quality and accessibility; troubleshoot, test, and recommend tools to meet analytics and reporting needs.

Design, build, and maintain large-scale data structures, pipelines, and ETL workflows. Integrate data from multiple sources, ensure data quality, support Data Science by building data marts/models, optimize Hadoop/HDFS queries, and collaborate on backend development and deployment.

Lead design, build, and operationalization of a scalable enterprise data platform and pipelines (batch and streaming) to support analytics and AI. Drive architecture for lakehouse, ETL/ELT, CDC ingestion, observability, governance, and mentor engineers while collaborating with cloud, MDM, and product teams.

Design, build, and support an AI-ready enterprise data enablement platform using Databricks, dbt, Delta Lake, and Azure data services. Create reusable CI/CD and IaC accelerators, govern data products via Unity Catalog, enable analytical access with Starburst/Trino, troubleshoot production issues, and collaborate with cross-functional teams to deliver governed, reusable, and production-grade data products for analytics and GenAI use cases.

The Data Engineer will manage production data quality tasks, support daily data loads, build ETL pipelines, and coordinate with teams to resolve data issues in an Azure Cloud-based environment.

Provide technical and business support for Citi applications, manage application environments, perform risk analysis, and lead enhancements while ensuring compliance with corporate standards.

The Senior Data Engineer will design, build, and optimize data infrastructure and pipelines, manage cloud data platforms, and work on AI projects, ensuring high data quality and efficient integration across systems.

The Senior Data Engineer will design and optimize data infrastructure on cloud platforms, manage large-scale data projects, and support AI initiatives. Responsibilities include developing ETL pipelines, implementing data solutions, optimizing performance, and mentoring junior engineers.

The Data Engineer will design and maintain data pipelines using Big Data technologies, optimize ETL processes, and apply AI techniques. Responsibilities include collaborating on data solutions and ensuring data quality and security.

The role requires designing, building, and maintaining data pipelines using Big Data technologies. Responsibilities include developing ETL processes, ensuring data integrity, collaborating with teams, and applying AI techniques to drive efficiency.

The Full Stack Python Developer will lead the team to build scalable data solutions, mentor junior developers, and implement application systems in collaboration with the Technology team.

The Senior Data Engineer will design, build, and maintain scalable data solutions, conducting feasibility studies, managing the development process, and ensuring high-quality code and compliance with industry standards.

The Data Solutions Engineer develops Big Data solutions, migrating workloads to cloud platforms and collaborating with stakeholders for optimal data implementations.