Data Engineer Jobs in Plano, TX

Design, build, and maintain AI agents, applications, and automations for finance stakeholders. Translate ambiguous business questions into decision-ready answers, shape data models and semantic views (dbt on Snowflake), prototype rapidly with AI coding tools, and partner closely with business users to deliver production-grade solutions.

Design, build, test, and maintain secure, scalable data pipelines and architectures. Work with Python, Databricks, SQL/NoSQL, and enterprise AI capabilities to deliver trusted data collection, storage, access, and analytics. Validate AI-assisted outputs, update data models, customize tools, and ensure controls, traceability, and data sensitivity handling.

Design, develop, and operate a production agentic data platform using Spark, Kafka, Airflow, Snowflake and AWS. Write secure high-quality code across frontend and backend, leverage enterprise AI-assisted development and LLMs (Claude Code, agents, RAG, embeddings), build autonomous agents and MCP server components, apply secure/responsible AI practices, troubleshoot systems, and own full-stack application/platform delivery with CI/CD, containers, and observability.

Design, develop, test, and maintain scalable data pipelines and architectures. Ensure data quality, lineage, governance, and security. Build automated testing/monitoring, optimize storage and query performance, update data models, and support data integration using SQL/NoSQL, cloud, and distributed processing frameworks.

Leads architecture and hands-on development of scalable ML and generative AI platforms. Builds Python APIs and SDKs, Databricks data and ML workflows, agentic AI capabilities, model integrations, infrastructure automation, and CI/CD pipelines. Ensures reliability, monitoring, security, performance, and cost efficiency while mentoring engineers, partnering with data science and product teams, and establishing AI-assisted engineering quality standards.

Lead the design and development of scalable, fault-tolerant streaming data platforms, distributed data pipelines, analytics data stores, and cloud-based solutions. Lead engineers, establish data architecture and pipeline best practices, develop and review high-performance code, and collaborate across Agile, product, security, and engineering teams. Mentor developers, support machine learning and microservices initiatives, and apply emerging technologies to deliver reliable data solutions.

Leads development of secure, scalable software for a distributed multi-tenant platform, including APIs, data ingestion, pipelines, dashboards, and isolated AWS deployments. Designs and operates cloud-native Kubernetes workloads, promotes automation and AI-assisted engineering practices, and champions security, privacy, and supply-chain controls. Evaluates architectures, mentors teams, improves operational stability, and supports platform delivery across regulated environments.

Lead and audit optical infrastructure work in customer data centers and co-locations, including fiber installation, migrations, remediation, decommissioning, break/fix, remote hands, testing, and troubleshooting. Interact with customers and vendors, provide service updates, interpret procedures and network documentation, and ensure quality, safety, accurate cabling, and operational standards. The role requires hands-on fiber expertise, customer communication, physical equipment handling, and travel to non-home-market sites for extended periods.

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.

Lead design and deliver enterprise data governance: implement data models, metadata standards, data cataloging/lineage, governed ETL/ELT pipelines, data quality controls, and coach teams on AI-assisted engineering and secure practices.

Lead design and deliver AI-enabled data platform capabilities (metadata, lineage, data quality, retention). Drive adoption of enterprise-authorized AI-assisted engineering, build agentic production systems, develop secure production code, automate remediation, establish production SRE controls, and mentor teams on responsible AI and validation standards.

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.

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.

Leads the design, development, and operation of scalable data platforms, pipelines, and control-plane services using Python, PySpark, SQL, Databricks, and AWS. Builds APIs, SDKs, orchestration, governance, observability, and automated remediation capabilities. Ensures data quality, security, lineage, CI/CD, and production reliability while mentoring engineers and promoting responsible AI-assisted development practices.

Designs, develops, reviews, and troubleshoots secure production software. Delivers Python-based solutions on AWS and Snowflake, drives automation and operational stability, leads engineering best practices, uses AI-assisted development tools responsibly, and contributes to product vision and team culture.

Design, develop, and maintain secure, production-quality software and AWS Data Lake solutions. Build infrastructure as code (Terraform), write Python/Java code, apply CI/CD and secure SDLC practices, analyze large data sets, and use enterprise AI-assisted coding tools while ensuring code quality, resiliency, and security.

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.

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 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.

Design, build, and optimize scalable AWS data pipelines and serverless solutions for utilities datasets. Orchestrate workflows (Step Functions/Airflow), implement ETL/ELT with PySpark/Python, model data for analytics, ensure performance, reliability, security, and compliance while collaborating with cross-functional stakeholders.

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.

Build, test, and operationalize complex data pipelines and solutions on cloud platforms. Analyze and transform data, ensure data governance, security, and quality, troubleshoot defects, and collaborate across teams to support analytics, ML/AI, and business intelligence needs.

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.