Data Engineer Jobs in Richardson, TX

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 support scalable cloud data platforms, pipelines, and applications. Develop distributed, real-time, and warehouse data solutions using Python, Spark, SQL, Databricks, Snowflake, and related technologies. Establish data engineering patterns, security controls, observability, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists while mentoring team members and communicating technical concepts to stakeholders.

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.

Design, develop, and maintain scalable full-stack applications using Java or Python. Build cloud-based data pipelines and analytics solutions with Databricks and Apache Spark. Collaborate with cross-functional teams to deliver reliable, high-performance software, evaluate emerging technologies, and explore AI-assisted and Agentic AI development capabilities.

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.

Leads data architecture and engineering initiatives for Payments Operations. Responsibilities include defining data requirements and quality standards, identifying authoritative sources, creating conceptual and logical models, designing data flows, preparing data with Python, PySpark, and SQL, and partnering with technology, product, analytics, and business teams to deliver data-driven solutions.

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, 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.

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.

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 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 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.

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 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 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.

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.

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.

Designs and maintains scalable data pipelines, models, integrations, and ETL/ELT solutions using cloud data technologies. Analyzes complex data, implements quality controls and automated testing, troubleshoots data issues, documents data flows, and supports governance. Collaborates with product owners, stakeholders, architects, data scientists, and engineers in an agile environment while applying AI-assisted development responsibly.

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 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.

Designs, develops, and maintains scalable AWS data platforms, data lakes, and pipelines. Builds workflows with Glue, S3, Lambda, and Step Functions; implements governance and access controls through Lake Formation; automates infrastructure with Terraform; and develops Python and Boto3 integrations. Manages Snowflake platforms, CI/CD deployments, testing, monitoring, troubleshooting, and production optimization using CloudWatch and Splunk. Collaborates with engineering and architecture stakeholders while using AI-assisted development tools.