Top Data Engineer Jobs in Dallas, TX

Designs, builds, and operates scalable cloud data platforms, APIs, pipelines, and data products. Leads architectural decisions, evaluates platforms such as Snowflake and Databricks, develops distributed and streaming workloads, enforces engineering standards, and mentors data engineers. Collaborates with product managers, software engineers, analysts, and data scientists to deliver resilient solutions. Requires extensive experience with application development, distributed data, SQL, programming, data pipelines, data modeling, cloud platforms, and relational and NoSQL databases.

Designs, builds, and maintains scalable data pipelines, microservices, and cloud-based data systems. Develops real-time and batch processing solutions using Python, Java, and AWS services; creates data integrations and workflows; translates requirements into system designs; implements testing and validation; and monitors distributed pipelines for reliability, performance, and data quality.

Design, build, and operate scalable cloud data platforms and pipelines using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Develop distributed, real-time, and secure data solutions; enforce engineering standards; collaborate with product, engineering, analytics, and machine learning teams; communicate technical outcomes; and mentor data professionals.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with data scientists, analysts, engineers, and product managers; implement data modeling, security, observability, and distributed processing solutions; enforce engineering standards; communicate technical outcomes; and mentor data community members.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Lead architectural decisions, establish reusable engineering patterns, evaluate technologies such as Snowflake and Databricks, and deliver distributed, real-time, and warehouse solutions. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical outcomes to stakeholders.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Design secure, resilient, and reusable data engineering solutions; support distributed and streaming workloads; enforce engineering standards; collaborate with product, software, analytics, and data science teams; communicate technical concepts; and mentor other engineers.

Build and architect scalable cloud data solutions, pipelines, platforms, and data products. Lead large-scale data initiatives, make architecture and platform decisions, ensure performance and operational efficiency, and apply modern technologies such as Python, Spark, Databricks, and Snowflake. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical concepts to stakeholders.

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 operate scalable cloud data pipelines, platforms, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data warehousing, security controls, observability, and real-time pipelines. The engineer collaborates with Agile teams, product managers, analysts, and software engineers; establishes reusable engineering patterns; communicates technical concepts; mentors peers; and independently delivers resilient data solutions.

Designs and leads scalable batch and real-time data pipelines supporting AI/ML applications, LLMs, RAG workflows, predictive models, and executive data products. Responsibilities include data architecture, feature stores, governance, security, lineage, performance and cost optimization, and cross-functional technical leadership. The role partners with data scientists, program managers, executives, and engineering teams to deliver reliable enterprise data solutions.

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.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Designs trusted Snowflake data products and enterprise semantic models that standardize business concepts, metrics, relationships, and rules. Builds Cortex semantic views and agents, validates AI-generated results, develops regression tests, and maintains metadata, lineage, and documentation. Leads modeling initiatives from discovery through production while partnering with business and technical stakeholders to improve analytics, automation, and AI consistency.

Build and maintain data pipelines into Snowflake, support orchestration, testing, and CI/CD, and partner with data scientists on machine learning pipelines. Monitor and troubleshoot pipeline health, improve reliability and performance, and ensure data is accurate, documented, and accessible. The role requires clean, tested, version-controlled code and collaboration with analysts and business teams. This is an entry-level, in-office position in Dallas.

Lead the design, development, and maintenance of scalable batch and streaming data pipelines and cloud data architectures. Drive workflow orchestration, data governance, modeling, cataloging, access control, performance optimization, and regulatory compliance. Translate business requirements into data lake and warehouse solutions, perform quantitative analysis, mentor engineers, establish technical standards, and guide AI-assisted, auditable data engineering practices.

Leads the design, development, and operation of scalable enterprise data products using Databricks and modern data engineering practices. Provides hands-on technical leadership across architecture, implementation, testing, troubleshooting, production readiness, observability, governance, and performance. Partners with architects and product teams to deliver analytical, operational, reporting, and AI-driven solutions. Establishes engineering standards, promotes responsible AI-assisted development, mentors engineers, and improves platform reuse and productivity.

Develop and manage Workday PSA and Financials data solutions, including conversions, transformations, integrations, reporting, APIs, EIBs, and ETL processes. Create technical documentation, perform data analysis and loading, support migrations and M&A initiatives, and ensure security, compliance, access controls, and data quality. Partner with business analysts, functional leads, engineers, and security teams while managing multiple projects and serving as a Workday data subject matter expert.

Designs, develops, and maintains scalable AWS data platforms, data lakes, and pipelines using Glue, S3, Lambda, Step Functions, Python, Snowflake, and Terraform. Implements governance and security through Lake Formation, automates infrastructure and deployments with CI/CD, and monitors production workloads using CloudWatch and Splunk. Collaborates with engineering and architecture teams to deliver reliable data solutions, leveraging modern AI-assisted development tools.

Build and support scalable data platforms, pipelines, warehouses, lakes, integrations, and transformation processes. Improve data quality, observability, testing, monitoring, security, and platform performance. Collaborate with analysts, data scientists, engineers, product teams, and business stakeholders to deliver trusted data solutions. Opportunities may include cloud data engineering, architecture, streaming, analytics engineering, governance, and technical leadership. This is a proactive talent community posting for future opportunities, with travel up to 10%.

Leads end-to-end data enablement for Supply Chain and Manufacturing domains. Partners with business and technical stakeholders to define use cases, roadmaps, data models, semantic layers, integration requirements, quality controls, and governed data products. Builds and optimizes Databricks lakehouse pipelines using SQL, Python, PySpark, and Delta Lake across complex ERP, manufacturing, procurement, inventory, and finance systems. Establishes standards for lineage, security, documentation, reliability, and production readiness.

Lead the design and development of scalable data pipelines, microservices, cloud-native ingestion systems, and streaming solutions. Collaborate with architects, engineers, product teams, and business stakeholders to build reliable data platforms using Python, Java, AWS services, SQL, and relational and NoSQL databases. Establish data quality, testing, monitoring, automation, and validation frameworks while optimizing performance and resolving production issues across distributed systems.

Lead design and build scalable data processing and control-plane solutions on Databricks/AWS. Develop Python/PySpark/SQL pipelines, APIs/SDKs, governance, observability, CI/CD, and operational runbooks. Mentor engineers, drive adoption of AI-assisted development, and ensure data quality, security, lineage, and platform reliability.

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.