Top Data Engineer Jobs in Dallas, TX

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business insights.

Lead design and implementation of scalable data infrastructure, pipelines, and transformations using cloud platforms. Build and optimize data warehouses (Snowflake, Databricks), orchestrate workflows (Airflow, Hadoop), ensure data quality and security, and coach teams to deliver analytics solutions that drive tax practice insights and client outcomes.

Design and build data infrastructure, pipelines, and transformations using Azure Data Factory, Azure Databricks, and Snowflake. Develop data architecture and modeling, maintain data lakes/warehouses, ensure data quality and security, optimize performance, and collaborate with clients to deliver tailored data solutions and governance.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Develop and modernize scalable batch and real-time data pipelines and feature engineering solutions on Databricks. Build feature store capabilities, migrate legacy Spark and EMR workloads, implement cloud-native AWS solutions, and apply testing, CI/CD, observability, governance, security, and data-quality controls. Collaborate with data scientists and stakeholders, contribute to architecture decisions, support production systems, and mentor junior engineers.

Design, build, and maintain optimized, highly available data pipelines and the BMG data warehouse. Lead medium-to-complex data engineering initiatives, perform solution design, code reviews, QA, data integration, and collaborate across teams. Support cloud migration and ensure compliance with data policies for reporting and analytics.

Design, build, test, and maintain secure, scalable data pipelines and architectures. Work with Python, Databricks, SQL/NoSQL, and enterprise AI capabilities to deliver trusted data collection, storage, access, and analytics. Validate AI-assisted outputs, update data models, customize tools, and ensure controls, traceability, and data sensitivity handling.

Build and maintain analytics data models, warehouse tables, views, and automated pipelines using dbt and Snowflake. Ensure data quality, consolidate business-unit data, develop ThoughtSpot dashboards, and analyze complex datasets to support product and operational decisions. Partner with product, engineering, and operations stakeholders, define metrics, improve self-service analytics, leverage AI tools for automation, and document data processes.

Designs and maintains scalable data pipelines, microservices, cloud data platforms, and real-time or batch processing systems. The role uses Python, Java, AWS services, SQL, streaming technologies, automated testing, data quality controls, monitoring, and CI/CD. Responsibilities include solution architecture, data integration, stakeholder collaboration, performance tuning, production support, and ensuring reliability across distributed data ecosystems supporting construction autonomy initiatives.

Lead design and deliver enterprise data governance: implement data models, metadata standards, data cataloging/lineage, governed ETL/ELT pipelines, data quality controls, and coach teams on AI-assisted engineering and secure practices.

Build and maintain batch and streaming data pipelines supporting enterprise analytics and AI. Develop transformations, analytical data models, quality checks, integrations, and reliable production workflows. Monitor and troubleshoot pipeline issues, support data-source onboarding, optimize platform performance and costs, and follow governance and security standards. Collaborate with BI developers, analysts, and data scientists while writing tested, documented code in an Agile environment.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Build and optimize scalable data platforms, database architectures, and ETL/ELT pipelines using SQL Server, Azure Data Factory, Azure services, and Python. Integrate healthcare and enterprise system data, perform profiling and transformation, implement master data management, establish lineage and monitoring, and support AI/LLM-based document extraction. Collaborate with stakeholders and engineers to deliver reliable, well-tested, documented data solutions.

Owns dbt-based transformation, dimensional modeling, and analytical data infrastructure across finance and land initiatives. Builds scalable AWS data pipelines, optimizes Snowflake storage and retrieval, develops SQL and Python solutions, and collaborates with stakeholders, data scientists, and BI engineers. Responsibilities include ETL, data warehousing, reporting infrastructure, cloud resource management, workflow automation, data security, CI/CD, and support for emerging agentic data products and MCP-based access.

Design, develop, test, and maintain scalable data pipelines and architectures. Ensure data quality, lineage, governance, and security. Build automated testing/monitoring, optimize storage and query performance, update data models, and support data integration using SQL/NoSQL, cloud, and distributed processing frameworks.

Participates in a 12-month new-graduate development program while designing, building, deploying, and operating data architectures and pipelines. Develops processes for capturing, transforming, storing, and maintaining structured and unstructured data across distributed and local systems. Applies automation, big-data, artificial intelligence, and machine-learning techniques to support data integrity, compatibility, and evolving business requirements.

Leads data engineering initiatives by designing, developing, testing, implementing, and supporting cloud-based data solutions. Collaborates with Agile teams, product managers, and machine learning engineers; works with distributed systems, databases, streaming applications, and data warehouses. Performs code reviews and unit testing, optimizes performance, explores emerging technologies, and mentors engineering team members.

Designs, builds, tests, maintains, and optimizes large-scale data pipelines, ETL processes, ingestion workflows, and cloud data storage. Develops Databricks data engineering and AI/ML pipelines, Azure Data Lake solutions, Power BI dashboards, and CI/CD automation. Ensures data quality, security, scalability, cost efficiency, and reusable engineering practices while collaborating with distributed technical teams.

Build and maintain scalable Microsoft Fabric data pipelines, ingestion frameworks, Delta Lake architectures, dimensional models, and Power BI semantic models. Optimize performance, refresh strategies, capacity usage, monitoring, and data quality while supporting client analytics SLAs. Collaborate with BI developers, analysts, and stakeholders to translate requirements into features, participate in agile ceremonies, and follow Git, CI/CD, code review, and SDLC practices.

Builds and maintains scalable ETL processes, database systems, data products, and real-time or offline analytics tools. Integrates large-scale data, ensures pipeline reliability and data integrity, translates business requirements into maintainable code, and supports analytical solutions such as Tableau dashboards. Partners with stakeholders, troubleshoots production issues, conducts testing and releases, provides data guidance, and trains users on tools and data sources.

Designs, develops, and optimizes scalable Azure-based data engineering solutions, including lakehouses, warehouses, ETL/ELT pipelines, Databricks notebooks, and analytics workflows. The role supports enterprise reporting, AI and machine learning initiatives, feature engineering, Generative AI, and MLOps. Responsibilities include performance tuning, CI/CD automation, production support, technical design, lifecycle management, stakeholder collaboration, and translating business requirements into secure, reliable data platforms.

Designs, develops, and maintains scalable ETL pipelines, databases, and cloud data infrastructure. Integrates data from APIs, third-party services, and on-premises or cloud databases. Develops automated processing workflows, optimizes data warehouses, improves data quality, supports analytics users, and performs testing and production deployment activities. Collaborates with data scientists, analysts, stakeholders, and vendors to deliver reliable, accessible data solutions.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Build and maintain healthcare data ingestion pipelines and dbt transformation models in Snowflake. Contribute to fact and dimensional modeling, semantic-layer development, data quality troubleshooting, pipeline reliability, and governed reporting. Collaborate with engineers, analysts, and stakeholders to translate requirements into technical data structures. Use AI-assisted development tools while following testing, documentation, security, SDLC, and change-management practices.

Design, build, and operate scalable batch and streaming data pipelines and analytical data models. Lead data architecture across ingestion, transformation, storage, and serving layers while implementing testing, CI/CD, observability, quality, lineage, governance, and access controls. Integrate restaurant and enterprise data sources, optimize platform performance and cost, deliver production-ready datasets for BI and AI, mentor engineers, and collaborate with cross-functional technical stakeholders.