Top Data Engineer Jobs in Dallas, TX

The Data Engineer Senior role involves developing data solutions, leading analysis efforts, and designing data service infrastructures within financial services using various technologies.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

Lead and mentor engineering teams to design, build, test, and support cloud-based data platforms and streaming solutions. Implement big data, ETL, and data warehousing using cloud services and distributed tools, collaborate with product managers, and ensure high-quality, performant code.

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.

Lead design, build, and operate reusable, secure, cloud-native data platforms on Google Cloud. Create ingestion, transformation, orchestration frameworks, enforce governance, optimize cost/performance, enable self-service analytics and AI, and provide enablement and runbooks for product and domain teams.

Lead design, build, and operation of scalable, secure GCP data platforms and reusable frameworks (ingestion, transformation, orchestration). Enable self-service analytics, enforce governance, data quality, observability, and security controls. Partner with domain teams, document patterns, and contribute to roadmap and tool selection.

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.

As a Lead Data Engineer, you will design and develop solutions, collaborate with teams on data technologies, and support cloud-based data warehousing and big data applications.

As a Data Engineer, you'll deliver scalable data solutions, participate in technical discussions, and ensure timely, trustworthy data delivery through robust pipelines.

Design, build, and maintain scalable cloud data pipelines and platform components to power analytics, ML, and applications. Implement transformations, data quality checks, lineage and governance, optimize performance and cost, monitor platform health, and build reusable components across business units while collaborating with product and architecture teams.

Build and maintain scalable data pipelines, implement ingestion/transformation and data quality, design data models and export solutions, support production jobs, write tests, evaluate technologies, and collaborate with stakeholders to deliver analytics-ready data.

Build and maintain scalable data pipelines and enterprise DataMarts, apply advanced SQL to transform disparate sources into well-modeled, high-quality datasets, ensure data governance and integrity, optimize performance, and partner with analysts and stakeholders to enable reliable reporting and analytics.

Design, build, and operate cloud-native, domain-oriented data products and pipelines across Azure and AWS using Databricks and Snowflake. Lead migration from legacy platforms, implement batch and streaming processing with Spark, ensure security and governance compliance, promote best practices (CI/CD, testing, observability), and mentor junior engineers while collaborating with product, analytics, and security teams.

As a Lead Data Engineer, you'll design and build scalable data platforms, develop streaming data pipelines, and collaborate within Agile teams to implement data solutions.

As a Lead Data Engineer, you'll lead the design and implementation of scalable data platforms, build distributed streaming data pipelines, and collaborate on technical solutions using big data technologies and agile methodologies.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data solutions. Collaborate across Agile teams, mentor engineers, build distributed and streaming data systems, and optimize performance using big data technologies and data warehouses.

The Lead Data Engineer will design, develop, and support complex technical solutions using Java, Python, AWS, and big data technologies. Responsibilities include collaborating with Agile teams, mentoring, and delivering cloud-based solutions.

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 Data Engineer will design, build, and deploy scalable data solutions, optimize data flows, and implement ETL processes, leveraging various cloud platforms and technologies.