Top Data Engineer Jobs in Dallas, TX

Designs data models, creates reusable schemas, maintains data dictionaries, and translates business requirements into technical specifications to enhance data governance.

The Data Engineer III role involves designing and building scalable data pipelines on Azure Databricks, migrating legacy systems, and ensuring compliance with financial regulations, while mentoring others and producing technical documentation.

Design and build scalable data pipelines using PySpark on Databricks, migrate ETL systems, and develop analytics dashboards and Python applications.

The Lead Data Engineer will design and manage data pipelines, mentor others, define architecture, and enhance data solutions to support business needs.

As a Lead Data Engineer, you'll design and enhance data pipelines, collaborate with stakeholders, and oversee data architecture using various cloud technologies.

The Data Engineer will design and maintain scalable data pipelines, optimize ETL processes, and ensure data quality for analytics in healthcare.

The Lead Data Engineer oversees application system implementation and development, ensuring compliance and user support while guiding less experienced staff.

Develop and support cloud-based data analytics architecture, including Power BI reports and dashboards. Collaborate with stakeholders to ensure accurate data integration and reporting solutions.

Lead integration engineer focusing on planning, developing, and testing data systems. Collaborate with teams and ensure quality data management.

Lead and mentor technical resolution of complex Data & AI production issues for customers, deep-dive into data pipelines and AI workflows, troubleshoot Spark/Delta/DLT/Model Serving, influence product improvements, and guide architects to build performant, reliable Databricks-based solutions.

Lead design and delivery of enterprise data platforms using Snowflake and AWS. Architect ELT/CDC pipelines, implement Data Vault 2.0 and dimensional models, optimize Snowflake performance, mentor engineers, partner with stakeholders, and ensure operational reliability and observability for analytics and BI workloads.

The Lead Data Engineer will design, develop, and maintain ETL pipelines, ensuring effective data extraction, transformation, and loading from various sources, while collaborating with stakeholders and solving complex business issues.

The Lead Data Engineer will design and implement data solutions for imagery models, lead engineering efforts, collaborate with stakeholders, and ensure data quality and governance while optimizing data pipelines.

Lead design and implementation of scalable data architectures and pipelines (real-time and batch), mentor junior engineers, ensure pipeline reliability and security, adopt new tools, collaborate cross-functionally, and implement data governance.

Lead design and delivery of large data products and pipelines, mentor senior engineers, set standards and best practices, promote data/analytics across business units, collaborate on governance and curated data catalog, and present technical work externally.

Design, develop, and maintain AxiomSL data sources, mappings, and regulatory reports. Build scalable data pipelines, perform advanced SQL transformations, ensure data lineage and reconciliation, support audits and production L2/L3 operations, and drive automation and platform upgrades to meet regulatory requirements.

The Data Engineer will develop scalable data pipelines, design lake house models, optimize Power BI performance, and ensure client satisfaction through technical support and efficiency improvements.

The Data Engineer will design and implement data models, build ETL pipelines, and ensure data quality for manufacturing data analytics.

The Senior Data Engineer is responsible for designing, implementing, and optimizing data ingestion pipelines and database schemas, ensuring efficient integration and performance of operational data services.

The Senior Data Engineer will lead the design and migration of an enterprise data platform to AWS and Snowflake, ensuring high-quality data pipelines and collaboration across teams to meet business requirements.