Top Data Engineer Jobs in Dallas, TX

Design, build, and operate large-scale Hadoop-based data pipelines and analytics platforms. Ingest, transform, and analyze structured and unstructured data to support analytics, ML, and reporting. Ensure reliability, performance, and cost-effectiveness in production, and troubleshoot, document, and optimize data workflows.

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.

Design and build scalable Microsoft Fabric pipelines and lakehouse models, optimize Power BI semantic models and refresh strategies, enforce SDLC and CI/CD best practices, mentor teammates, and ensure SLA-driven, client-facing analytics reliability and performance.

Lead design and delivery of enterprise cloud data platforms using Snowflake and AWS. Architect Data Vault 2.0 models and scalable ELT/event-driven pipelines, mentor engineers, drive performance tuning and cost optimization, implement monitoring and observability, and partner with stakeholders to ensure reliable, production-ready analytics and reporting solutions.

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 own cloud-native data pipelines and dbt transformations on AWS and Snowflake. Write production-quality Python, ensure data quality and observability, collaborate with stakeholders and vendors during migration, and support infrastructure-as-code practices.

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 and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate with Airflow, ingest from 3rd-party APIs, optimize columnar formats and SQL, support ML workflows, consult stakeholders, and provision AWS/on-prem infrastructure with Terraform.

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.

Identify, evaluate, and integrate external data sources to enhance the platform. Build and maintain automated ETL pipelines, ensure data quality and governance, document workflows, perform cost-benefit analyses of vendors/tools, and present recommendations to leadership while collaborating with product and engineering teams.

The Data Engineer II will design, build, and maintain data pipelines, ensuring data governance and security. Responsibilities include coding in Python, managing data processing, and collaborating with data teams.

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.

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 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 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.

Lead design, operation, and reliability of production data pipelines in a multi-terabyte Snowflake environment. Translate client requirements into dimensional models, debug and remediate pipeline failures, evolve the data mart for event-driven and AI workflows, and improve pipeline trustworthiness via testing, CI/CD, audit logging, and documentation.

Lead design and implementation of large-scale data pipelines, ETL, and warehousing. Build and manage databases, ensure data quality/security, collaborate with analysts and scientists, evaluate new technologies, mentor junior engineers, and manage project delivery.