Top Data Engineer Jobs in Dallas, TX

The Lead Data Engineer at Lennar will design and implement data engineering solutions, lead the data platform team, and ensure data quality and accessibility for business insights.

Lead design and build of a client-facing analytics platform using Microsoft Fabric. Develop and maintain Fabric pipelines and notebooks, design lakehouse and dimensional models, optimize Power BI semantic models, enforce SDLC/CI-CD practices, ensure SLA reliability, and mentor team members while delivering scalable, client-focused analytics.

Lead design, maintenance, and optimization of on-prem and cloud data platforms. Perform DBA duties for SQL Server, manage HA/DR and replication, troubleshoot performance, lead on-prem to Azure migrations, maintain Azure data services and Microsoft Fabric, optimize cloud cost/performance, implement monitoring, collaborate with architects and BI teams, and mentor junior engineers.

The Senior Data Developer Tech Lead will design and manage scalable data solutions within Hadoop, lead streaming data architecture, optimize performance, and integrate AI/ML capabilities, while coaching team members and ensuring best practices in the banking domain.

Lead the design and implementation of Hadoop architecture and real-time data solutions. Coach team members, optimize performance, and ensure data governance within a small agile squad, focusing on AI/ML capabilities.

Design and implement scalable data pipelines, support data ingestion and quality, collaborate on data models, and maintain data exports, contributing to technology improvements.

Hands-on senior data engineer to design, build, and optimize Hadoop-based and real-time streaming data platforms (Kafka/Spark). Lead end-to-end delivery, integrate AI/ML and MLOps, enforce data security/governance in banking, mentor teammates, and improve performance and reliability of data pipelines.

Design, build, test and support batch and streaming data pipelines and curated datasets on a modern Lakehouse and AI data platform. Responsibilities include data modelling, optimisation, quality controls, reconciliation, platform tooling, and partnering with stakeholders to deliver reliable, production-ready data products.

Design, build, and optimize enterprise data platform components and ETL/data pipelines for regulatory reporting. Lead PySpark development, implement Azure DevOps CI/CD, apply TDD, support migration to Microsoft Fabric, perform data profiling, influence data architecture, and mentor data engineers.

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.

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, 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.

Analyze existing systems, design cloud-ready data processing workflows, develop specifications and migration tools, validate implementations via automated testing, and report progress. Collaborate with stakeholders and developers to optimize BI/reporting solutions and complete relevant technical training.