Top Data Engineer Jobs in Dallas, TX

Designs and maintains scalable data pipelines, develops dbt transformation workflows, and architects Snowflake data models and warehousing solutions. Integrates multiple data sources, implements quality monitoring, troubleshoots pipeline issues, optimizes SQL and warehouse performance, documents architecture, and collaborates with analysts, data scientists, and stakeholders to deliver reliable analytics infrastructure.

Lead deployment and administration of the electronic Quality Management System (eQMS), digitize quality workflows, and build analytics and BI solutions (Power BI) to monitor quality KPIs. Partner cross-functionally to implement automation, support UAT/validation/training, maintain data integrity and governance, and enable future predictive quality and AI-enabled decision support.

Design, architect, and deliver scalable data pipelines and storage solutions using big data and cloud technologies. Lead technical strategy, mentor engineers, ensure data governance, security, and performance, and collaborate with Data Science and Product teams to support analytics and ML initiatives.

Design, develop, and maintain ETL pipelines, databases, and data visualizations to support 300mm semiconductor manufacturing. Analyze operational performance, automate data extraction, troubleshoot systems, collaborate with stakeholders, and integrate emerging technologies to improve manufacturing efficiency.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

Design, build, test, deploy, and monitor batch and streaming data pipelines and ETL processes. Evaluate and adopt data technologies (including Adobe Experience Platform and CDPs), collaborate with data scientists, architects, and business partners to ingest and prepare large semi-structured/unstructured datasets for ML training, model execution, and search analytics, and implement automation, documentation, and tooling for scalable analytic data systems.

The Data Engineer will develop data pipelines, ensure data reliability, and support process automation while collaborating within Agile teams.

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.

The Data Engineer will analyze, develop, implement, test, and support data warehouse projects. Responsibilities include project support, data validation, and enhancing data management processes, all while collaborating with team members and improving performance.

Data Engineer will develop and support data warehouse projects, analyze data integrations, work with ETL tools, and improve processes while collaborating with team members.

Design, build, test, and operationalize complex data pipelines and multi-layer data architectures using Snowflake and dbt. Analyze source systems, perform data mapping and transformations, ensure data governance, quality, and security, resolve defects, optimize query and pipeline performance, and collaborate with engineering and business teams to support BI and AI/ML use cases.

Lead design and build scalable, low-latency, fault-tolerant data platforms and pipelines. Mentor engineers, influence data architecture and governance, build analytics stores using big-data and cloud technologies, collaborate with product and security teams, and ensure CI/CD, resiliency, testing, and performance for data-driven solutions.

Design, build, and optimize large-scale ETL/ELT pipelines and lakehouse architectures on cloud platforms using Databricks, Spark, Delta Lake, and Snowflake. Lead data federation with Starburst, improve performance and governance, support AI/ML projects (RAG/Agentic AI), mentor engineers, manage stakeholders, and deliver solutions in an Agile environment.

Lead design and implementation of scalable cloud data platforms and ETL/ELT pipelines using PySpark/Databricks, Delta Lake, and Snowflake. Optimize Spark workloads, enforce data governance, support AI/ML (including RAG and Agentic AI), manage data federation with Starburst, mentor engineers, and engage stakeholders in an Agile environment to deliver enterprise-scale analytics solutions.

Build, implement, and support enterprise data and ETL systems across the full development lifecycle. Conduct systems analysis, design data warehouse and cloud solutions, provide operational support, manage risks, and implement security controls. Collaborate with business and technology stakeholders, resolve complex technical issues, establish development standards, and mentor junior analysts. The role requires expertise in AbInitio, AWS orchestration tools, multiple data platforms, Python, PySpark, Unix scripting, and banking technology.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Design, build, and maintain enterprise analytics solutions and semantic data models using Microsoft Fabric, Databricks, SQL, Python, and BI tools. Deliver scalable reporting datasets, executive dashboards, data quality standards, and CI/CD practices. Lead architecture discussions, mentor engineers, collaborate with stakeholders, and drive analytics strategy to enable data-driven decisions.

Contribute to building and maintaining cloud-based data pipelines and data products. Develop, test, and optimize SQL and Python transformations, participate in code reviews, troubleshoot data quality issues, and follow CI/CD and documentation practices to support analytics and AI initiatives.

Design and build Microsoft Fabric medallion architecture, including Bronze, Silver, and Gold Delta tables, Spark feature pipelines, canonical MERGE workflows, Direct Lake semantic models, and OneLake layouts. Optimize Delta and Parquet physical storage, establish idempotent Python-based writes, size Fabric capacity, and support infrastructure provisioning with Terraform. The role also contributes to risk analytics delivery, performance testing, and potential ClickHouse adoption while partnering with DevOps and technical leadership.

Leads the design, development, and deployment of scalable cloud-native data pipelines for enterprise analytics and reporting. Establishes data engineering standards, governance, security, privacy, and compliance practices. Makes architectural decisions, mentors engineers, conducts reviews, translates business needs into technical solutions, and owns end-to-end data initiatives in an Agile environment.

Design, develop, and maintain enterprise data solutions and pipelines using Azure data platform and Microsoft Fabric. Support data modeling, master data management, medallion architecture, data quality, and integrations to produce trusted, scalable data for analytics and BI.

Design and maintain scalable ETL/ELT pipelines, develop data solutions using Databricks and Snowflake, and collaborate with cross-functional teams to ensure data quality and performance optimization.

The Senior Data Quality Specialist will develop data quality rules, implement cloud-based solutions, and analyze data accuracy, leading projects and collaborating with teams.

Build and own Snowflake data models and dbt pipelines for a Customer Data Platform supporting marketing segmentation and customer journeys. Integrate complex source systems, manage data architecture and governance, conduct data discovery, and translate business rules into scalable solutions. Collaborate with engineering, analytics, product, and business stakeholders; lead technical workshops; mentor junior engineers; and implement automated data quality testing, CI/CD workflows, and AI-augmented engineering practices.

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.