Data Engineer Jobs in Richardson, TX

Lead Data Engineer/DB2 DBA responsible for maintaining and optimizing mainframe Db2 databases and data pipelines. Tasks include troubleshooting DB2 issues, backup/recovery, DDL deployments, environment refreshes, performance tuning, control reviews, and collaborating with application teams. Uses enterprise-authorized AI to accelerate design and operational tasks while ensuring validation, security, and auditability.

Design, build, and optimize scalable batch and streaming data pipelines and workflows. Drive data governance, modeling, cataloging, access control, and performance tuning. Mentor engineers, translate business needs into data-lake/warehouse solutions (AWS/Snowflake), use enterprise AI responsibly to accelerate SDLC tasks, and collaborate with analytics and business stakeholders to support regulatory and business objectives.

Lead an agile engineering team to design, develop, and operate secure, scalable applications. Provide technical guidance, write and review production code, drive AI-assisted engineering adoption, influence product design, and promote firmwide SDLC practices using cloud-native, microservices, and Big Data technologies.

Lead an agile team to design, build, deploy, and operate cloud-native data and ML platforms. Provide technical guidance, write and review secure production code, drive AI-assisted engineering adoption, migrate legacy big-data apps to cloud, build data pipelines and monitoring/provisioning tools, and influence architecture and automation across the SDLC.

Lead design and implement secure AWS Data Lake solutions, develop IaC and application code, promote AI-assisted engineering practices, analyze large datasets, collaborate across teams, and drive secure, scalable software delivery and improvements.

Design, build, and maintain cloud-native ETL/data pipelines and integrations using AWS Glue, Java or Python. Deploy and optimize containerized applications on Amazon ECS, maintain CI/CD (Jenkins, Docker, Git), and support production incidents. Analyze large datasets to inform business decisions and collaborate with stakeholders to translate requirements into technical solutions.

Lead and mentor a data engineering team to design, develop, test, and support cloud-based big data and streaming solutions. Build full-stack data pipelines using Java/Scala/Python, RDBMS/NoSQL, Redshift/Snowflake, and cloud platforms. Collaborate with product managers, perform unit tests and code reviews, and promote engineering best practices and tech community engagement.

Designs and delivers secure, scalable AWS data-platform software. Writes production code, builds data processing using Glue/EMR/Redshift/Athena/Lake Formation, leverages AI-assisted dev tools responsibly, produces architecture artifacts, and drives CI/CD, resiliency, and security improvements.

Lead and mentor data engineering teams to design, build, test, and support cloud-based, full-stack data solutions. Work with big data, streaming, and data warehousing technologies (Redshift/Snowflake), collaborate with product managers, and ensure high-quality, performant code and systems.

The Lead Data Engineer will design, develop, and support technical solutions, utilizing big data technologies and cloud platforms, while collaborating with Agile teams.

Lead Data Engineer designs, develops, tests, and supports cloud-based data solutions. Collaborates with Agile teams, mentors engineers, implements big data and streaming systems, and optimizes code for performance to deliver data-driven products.

Lead design and build scalable, low-latency streaming data platforms and distributed data pipelines. Mentor engineers, define data architecture and best practices, collaborate across agile teams, implement cloud-based analytics stores, and ensure CI/CD, resiliency, and security for production streaming and batch data systems.

Lead Data Engineer designs, develops, tests, and supports cloud-based data solutions. Collaborates with Agile teams on distributed, real-time, and ML-enabled systems using languages and data platforms (Java, Scala, Python, Go, Redshift, Snowflake). Mentors engineers, performs code reviews and unit tests, and drives technical innovation and experimentation.

Design, build, and maintain scalable data pipelines and applications on AWS and Databricks. Develop production-quality Python code, process large datasets with Spark, implement IaC CI/CD with Terraform, and apply AI-assisted development responsibly. Troubleshoot data workflows, create architecture artifacts, and contribute to engineering best practices and team collaboration.

Design, develop, test, implement, and support cloud-based data solutions. Lead and mentor engineering teams, build distributed and streaming data pipelines, optimize performance, and collaborate with product managers to deliver scalable data platforms.

Design, build, and maintain secure data pipelines for structured and unstructured sources. Segment and protect data domains, enforce access controls and classification, and enable analytics/AI use cases. Optimize workflows for performance, observability, and resiliency across cloud and hybrid platforms. Partner with stakeholders to translate compliance and business needs into reusable, governed data solutions and support production systems with strong attention to integrity, lineage, and auditability.

Lead design and build scalable, low-latency, fault-tolerant streaming data platforms and distributed data pipelines. Own streaming analytics, cloud-based data stores, CI/CD practices, and mentor engineers while collaborating with product, security, and ML teams to deliver robust cloud solutions.

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.

Lead Software Engineer responsible for designing, building, and maintaining scalable data pipelines and platform components (cataloging, quality, lineage, governance). Implements data modeling, partners with stakeholders to deliver production-grade code, automates operations, evaluates vendors, and leads Agentic Autonomous Lakehouse and engineering communities to drive platform innovation and operational stability.

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.

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.

Lead development and operation of scalable ETL/data transformation pipelines using Spark and data lake technologies. Design, automate, monitor production workflows, improve cost and reliability, mentor junior engineers, and contribute to platform/SDK infrastructure and CI/CD while enforcing secure, AI-assisted engineering practices.

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 engineer responsible for designing, building and operating scalable Big Data/ETL pipelines and production services on AWS. Drive automation (including LLMs/agents), SRE practices, security and data accountability, monitoring/alerting, incident management, and cross-team coordination. Champion AI-assisted engineering, remediate control/security findings, and improve reliability, performance, and operational efficiency.