Data Engineer Jobs in Plano, TX

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.

Develop secure, scalable full-stack data platforms and applied AI capabilities within an agile team. Build agentic applications, AI agents, MCP servers, and LLM-powered features using RAG, embeddings, evaluation, guardrails, and tool calling. Implement frontend and backend services, data engineering pipelines, cloud-native platform capabilities, CI/CD, observability, and controlled releases. Analyze large datasets, troubleshoot technical issues, and validate AI-assisted development outputs through testing, peer review, and secure coding practices.

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.

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.

Build and operate production platforms for LLM-powered agents and AI features. Responsibilities include developing reusable services, evaluating and testing LLM systems, implementing observability, applying security and governance controls, and improving AI-assisted engineering practices. The role partners with product, engineering, data, security, and compliance teams to deliver scalable solutions, with preferred experience in orchestration frameworks, RAG, vector databases, Docker, Kubernetes, AWS, and Databricks.

Leads a 50+ person organization across data engineering, ML, GenAI, and security products. Owns multi-year strategy, architecture, technical roadmaps, hiring, and delivery standards. Designs low-latency identity decisioning and threat-detection systems, robust ML infrastructure, streaming pipelines, MLOps practices, AI security capabilities, and automated governance. Partners with executive and security leadership to address regulatory priorities, emerging cyber risks, and protection of human, workload, and AI-agent identities.

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.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed and streaming data workloads, data models, and relational and NoSQL solutions using Python, Spark, SQL, AWS, Databricks, and Snowflake. Establish reusable engineering patterns, security controls, and operational standards while collaborating with engineers, product managers, analysts, and data scientists. Mentor colleagues, communicate technical outcomes, and deliver resilient data products with minimal supervision.

Build and maintain data engineering solutions using Python, PySpark, and big data technologies. Work with AWS services including S3, EMR, and Lambda, while supporting infrastructure through Terraform. Integrate Java, REST APIs, messaging systems, Kafka, Cassandra, Airflow, Control-M, and native schedulers. Databricks experience and familiarity with machine learning workflows are preferred.

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.

Leads software engineering projects and developers building distributed, full-stack, cloud-based systems for cloud operations resilience. Designs microservices, APIs, data and streaming applications, and reusable frameworks using AWS and open-source technologies. Provides technical leadership across Agile teams, establishes engineering standards, mentors developers, improves system performance, reduces technical debt, and partners with architects and product managers on long-term technology strategy.

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.

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.

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.

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 a greenfield data platform using Medallion Architecture, integrating SQL Server and Oracle sources through CDC ingestion. Develop dbt Bronze, Silver, and Gold layers, migrate Alteryx workflows, configure Iceberg and Airflow, and manage metadata and lineage. Create an MCP server for AI-agent access to business data marts, automate Excel reporting with Python, and support governed analytics and GenAI initiatives.

Build and operate a greenfield data platform integrating enterprise systems through Bronze, Silver, and Gold medallion layers. Develop dbt and Python transformations, Airflow pipelines, Iceberg tables, data marts, governance and lineage capabilities, and migrate Alteryx workflows to code. Create an MCP server that enables AI agents to query business data marts and support AI-driven analytics. Automate Excel reporting and ensure scalable, reliable, and governed data infrastructure.

Designs, deploys, configures, and maintains managed data-services solutions across complex multi-site networks. Translates customer requirements into Layer 2 and Layer 3 technical specifications, develops automation scripts, designs IP addressing schemes, and troubleshoots service-impacting issues. The role supports technologies including BGP, VPLS, VPRN, DIA/IES, Meraki, VeloCloud, Fortinet, and Adtran. Responsibilities include solution design, configuration documentation, customer and vendor collaboration, root-cause analysis, and rotating on-call support.

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.