Top Data Engineer Jobs in Dallas, TX

Designs, builds, and operates scalable, resilient cloud data platforms and pipelines. Leads large-scale data initiatives, makes architectural decisions, evaluates technologies such as Snowflake and Databricks, and develops solutions using distributed data systems, relational and NoSQL databases, and cloud platforms. Collaborates with product and engineering teams, establishes reusable design patterns, communicates technical concepts, and mentors data engineers, analysts, and scientists.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Architect reusable engineering patterns, improve resilience and operational efficiency, enforce data security, and support real-time workloads. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical outcomes to stakeholders.

Design, build, and optimize end-to-end data pipelines and architectures using Databricks, Spark, AWS, Airflow, and Kafka. Develop ETL processes, data models, warehouses, and reliable Python and SQL solutions while ensuring data quality, security, and governance. Collaborate with data scientists, analysts, and stakeholders to support analytics and AI/ML initiatives. Monitor workflows, troubleshoot issues, validate AI-assisted outputs, and improve performance and resiliency.

Lead the design and development of scalable, low-latency real-time data platforms and distributed streaming pipelines. Build data solutions using Flink, Spark, Kafka, Snowflake, AWS services, and related technologies. Guide engineers, establish data architecture and pipeline best practices, develop cloud-based solutions, support Agile delivery, perform testing and code reviews, and mentor team members. The role also involves data modeling, observability, data warehousing, and collaboration with product, security, and engineering teams.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, warehousing, streaming, security, and observability. The engineer collaborates with Agile teams, architects reusable engineering patterns, communicates technical outcomes, mentors colleagues, and delivers resilient data solutions using technologies such as Python, Spark, Databricks, and Snowflake.

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.

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.

Develop and maintain secure, scalable data pipelines, architectures, models, and analytics solutions. Review enterprise data controls, customize tools and products, validate AI-assisted engineering outputs, and ensure SDLC quality, auditability, resiliency, and security. Use advanced SQL, NoSQL databases, Python, and statistical analysis to support business needs and collaborate with Product Owners. AWS and modern front-end experience are preferred.

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, governance, observability, and production readiness. Partners with technical and business stakeholders, establishes engineering standards, promotes responsible AI-assisted development, mentors engineers, and supports analytical, operational, reporting, and AI-driven use cases.

Leads enterprise data engineering strategy, architecture, modernization, and governance. Designs scalable cloud-native data lakes, warehouses, lakehouses, ingestion pipelines, and real-time processing platforms. Establishes standards for data modeling, security, quality, observability, DataOps, DevOps, and compliance. Provides technical leadership across engineering teams, mentors senior professionals, drives transformation programs, and partners with business and technology stakeholders to deliver resilient data products supporting analytics, regulatory reporting, and operational intelligence.

Build and maintain data solutions across the full data engineering lifecycle, including requirements analysis, pipeline development, testing, deployment, scheduling, production support, and data reporting. Collaborate with business and senior engineering teams, participate in Agile ceremonies and code reviews, apply data management practices, and help ensure data integrity, risk management, and compliance.

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.

Supports data engineering through code debugging, troubleshooting, solution development, peer code reviews, and system performance improvements. The intern collaborates with technical teams, reviews documentation, learns emerging technologies, and communicates solutions to team members and business leaders. The role requires current enrollment in a relevant bachelor's or higher degree program, a minimum 3.0 GPA, and programming or version-control experience. The internship is hybrid and lasts 11 weeks.

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.

Migrates customer data from legacy and competitor systems into SaaS products using SQL-based tools. Performs extraction, transformation, cleanup, validation, auditing, data mapping, and quality assurance. Develops repeatable scripts and workflows, collaborates with customers and cross-functional teams, supports testing and defect resolution, and documents migration processes. The role increasingly uses BigQuery, cloud platforms, automation, and intelligent tools while supporting global delivery across regions and time zones.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Responsibilities include ETL development, data schema and process enhancements, load monitoring, troubleshooting, emerging technology evaluation, privacy compliance coordination, and support for analytics, data science, and search applications. The role collaborates with business, engineering, and compliance teams while using cloud, distributed processing, containerization, and modern data architecture technologies.

Supports data and software development initiatives through code debugging, performance improvements, troubleshooting, code reviews, documentation, and technical problem-solving. Collaborates with team members to develop reliable data systems, learn emerging technologies, and communicate project updates. The intern also evaluates and applies AI tools responsibly to improve workflows and insights.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Responsibilities include data transformation, schema and business-rule enhancements, load monitoring, troubleshooting data availability, evaluating emerging technologies, and collaborating with business, engineering, and privacy teams. The role also supports data governance, secure data handling, cloud technologies, and adoption of AI-enabled tools.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Builds ETL processes, data schemas, business rules, and cloud-based data solutions; monitors data loads and resolves availability issues. Collaborates with business, engineering, and privacy teams to ensure compliant data handling. Evaluates emerging data technologies, supports innovation, and helps adopt modern architectures, DevOps practices, and AI-enabled workflows.

Designs and optimizes data architectures, pipelines, databases, and Big Data solutions. The role develops partitioning and indexing strategies, supports scalable and secure data infrastructure, documents enterprise data standards, and collaborates with architects, engineers, product teams, and business stakeholders. Responsibilities include database performance tuning, distributed systems development, data modeling, and alignment with Agile delivery practices.

Leads end-to-end data engineering solutions, including pipeline design, data modeling, testing, deployment, optimization, production support, and data quality. Partners with stakeholders to develop cloud-based data platforms, prototypes, technical standards, and scalable analytic systems. Provides technical leadership, conducts design and code reviews, mentors team members, and ensures compliance with risk and data management policies.

Designs, develops, deploys, and supports scalable batch and streaming data pipelines using Databricks and lakehouse technologies. Builds governed data products for analytics, AI, machine learning, and enterprise insights. Responsibilities include data ingestion and transformation, orchestration, quality validation, observability, governance, performance optimization, troubleshooting, testing, documentation, and production support. Collaborates with engineers, architects, product owners, and business stakeholders across the full engineering lifecycle.

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.