Data Engineer Jobs in Richardson, TX

Leads software engineering and AI/ML delivery for large-scale risk technology systems. Designs complex architectures, develops secure production code, optimizes Spark clusters and data pipelines, and applies Python, PySpark, SQL, AWS, and Databricks. Establishes AI-assisted engineering governance, improves software quality and automation, analyzes large datasets, and leads technologists in delivering resilient, scalable applications.

Lead software engineering for cloud-based machine learning platforms. Design and maintain infrastructure supporting model development, training, deployment, serving, and monitoring on AWS. Automate testing, releases, and SDLC processes; monitor platform stability and resolve performance issues. Guide responsible use of AI-assisted development tools, contribute to architecture and design decisions, support platform automation, and coach engineers on secure, compliant engineering practices.

Designs, operates, and improves secure AWS cloud environments and ECS application platforms supporting data-driven applications. Builds and administers CI/CD pipelines using Jenkins and Spinnaker, improves reliability and scalability, supports Kafka and SQL dependencies, and partners with globally distributed Agile teams. The role also involves mentoring engineers, establishing reusable deployment standards, and driving operational excellence across Java/Spring Boot microservices and React micro frontends.

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.

Participates in a 12-month new-graduate development program while designing, building, deploying, and operating data architectures and pipelines. Develops processes for capturing, transforming, storing, and maintaining structured and unstructured data across distributed and local systems. Applies automation, big-data, artificial intelligence, and machine-learning techniques to support data integrity, compatibility, and evolving business requirements.

Analyze data using SQL and relational database tools; design tables, views, procedures, and database objects; support reporting; identify trends, anomalies, and outliers; organize information from multiple sources; and validate databases, program results, and reports for accuracy and completeness.

Build and maintain healthcare data ingestion pipelines and dbt transformation models in Snowflake. Contribute to fact and dimensional modeling, semantic-layer development, data quality troubleshooting, pipeline reliability, and governed reporting. Collaborate with engineers, analysts, and stakeholders to translate requirements into technical data structures. Use AI-assisted development tools while following testing, documentation, security, SDLC, and change-management practices.

Designs, develops, tests, deploys, and supports enterprise data products, pipelines, transformations, and analytics solutions using Databricks and cloud technologies. Builds governed Bronze, Silver, and Gold lakehouse assets; implements data quality, monitoring, security, and access controls; and develops reusable data models and engineering components. Collaborates with architects and Agile delivery teams, participates in reviews and CI/CD, troubleshoots performance issues, automates operational processes, and documents technical solutions. The role supports reporting, operational workloads, and AI/ML use cases.

Design and operate the data quality layer for a Databricks lakehouse using DQX. Translate legacy SQL validation logic into automated checks, quarantine invalid records, establish quality gates, integrate checks with batch and streaming pipelines, store results in Delta and Unity Catalog, build dashboards and alerts, and deploy version-controlled rules through CI/CD.

Own product strategy, roadmap, definition, business cases, and commercialization for semiconductor power management and motor driver solutions used in data center thermal management. Lead products from concept through release, drive design wins with hyperscalers, OEMs, ODMs, and partners, and develop technical marketing and sales enablement materials. Collaborate globally with engineering, sales, applications, and leadership teams while supporting customer engagements and traveling 25–35% internationally.

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.

Designs and delivers scalable, low-latency streaming data pipelines using Apache Flink, Confluent Kafka, and Apache Iceberg. Modernizes batch and RDBMS workflows into event-driven architectures, builds lakehouse data structures, ensures data quality, optimizes streaming performance, and integrates ITSM and enterprise systems. The role also leads testing, documentation, delivery lifecycle activities, stakeholder collaboration, CI/CD, operational monitoring, and mentoring of data engineers.

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.

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.

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.

Leads enterprise data engineering and AI-ready data knowledge initiatives across Network Services. Designs scalable pipelines, data models, metadata, governance, lineage, quality controls, and validation frameworks for structured and unstructured data. Partners with subject matter experts to prepare trusted context for AI and agentic workflows, guides hybrid data architectures, supports testing and releases, establishes control standards, and mentors data engineers.

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.

Design and maintain scalable ETL/ELT pipelines, develop data solutions using Databricks and Snowflake, and collaborate with cross-functional teams to ensure data quality and performance optimization.

Design, build, and optimize enterprise-scale data platforms and ETL/ELT pipelines within Azure and Microsoft Fabric. Architect ingestion, transformation, and serving layers, implement Data Vault 2.0 and medallion architectures, enable low-latency distributed processing, ensure data quality for high-volume transactional sources, and collaborate with architects, analysts, and stakeholders to deliver scalable analytics solutions.

Analyze existing systems, design cloud-ready data processing workflows, develop specifications and migration tools, validate implementations via automated testing, and report progress. Collaborate with stakeholders and developers to optimize BI/reporting solutions and complete relevant technical training.