Data Engineer Jobs in Irving, TX

Leads enterprise software engineering initiatives supporting financial forecasting, planning, and reporting. Designs, codes, tests, documents, and supports large-scale solutions using Oracle Essbase 21c, multidimensional planning applications, ETL, Python, PySpark, and object storage. Manages Essbase infrastructure, middleware, capacity planning, availability, performance, security, and production issues while establishing engineering standards, mentoring teams, and modernizing cloud-based data platforms.

Designs, builds, and maintains scalable data pipelines, microservices, and cloud-based data systems. Develops real-time and batch processing solutions using Python, Java, and AWS services; creates data integrations and workflows; translates requirements into system designs; implements testing and validation; and monitors distributed pipelines for reliability, performance, and data quality.

Leads the design, development, and operation of reusable, secure cloud data platforms on GCP. Builds standardized ingestion, transformation, orchestration, quality, governance, and observability frameworks using BigQuery, Dataflow, Dataproc, Pub/Sub, and Composer. Develops Python, Java, SQL, and Beam libraries; optimizes performance, cost, and reliability; implements data security controls; and partners with engineering, analytics, and ML teams to onboard use cases. Provides documentation, enablement, roadmap input, and occasional production support.

Leads enterprise data engineering strategy, architecture, modernization, and governance. Designs scalable cloud-native data lakes, warehouses, lakehouses, ingestion pipelines, and real-time processing platforms. Establishes standards for data modeling, security, quality, observability, DataOps, DevOps, and compliance. Provides technical leadership across engineering teams, mentors senior professionals, drives transformation programs, and partners with business and technology stakeholders to deliver resilient data products supporting analytics, regulatory reporting, and operational intelligence.

Build and support scalable data platforms, pipelines, warehouses, lakes, integrations, and transformation processes. Improve data quality, observability, testing, monitoring, security, and platform performance. Collaborate with analysts, data scientists, engineers, product teams, and business stakeholders to deliver trusted data solutions. Opportunities may include cloud data engineering, architecture, streaming, analytics engineering, governance, and technical leadership. This is a proactive talent community posting for future opportunities, with travel up to 10%.

Lead the design and development of scalable data pipelines, microservices, cloud-native ingestion systems, and streaming solutions. Collaborate with architects, engineers, product teams, and business stakeholders to build reliable data platforms using Python, Java, AWS services, SQL, and relational and NoSQL databases. Establish data quality, testing, monitoring, automation, and validation frameworks while optimizing performance and resolving production issues across distributed systems.

Develop and deploy AI-driven annotation, labeling, and captioning systems for robotics, computer vision, autonomy, and Vision-Language-Action datasets. Train and evaluate machine learning models, build scalable data pipelines, integrate human-in-the-loop workflows, and improve annotation quality. The role requires production software engineering, distributed systems, sensor-data expertise, and experience with computer vision or autonomous systems. Work is onsite in Irving, Texas, with relocation assistance and visa sponsorship available.

Develop and deploy AI-powered annotation, labeling, and captioning systems for robotics, computer vision, autonomy, and VLA datasets. Build data pipelines supporting human review, quality validation, and dataset publication; train and evaluate machine learning models; integrate Caterpillar systems with annotation providers; and ensure scalable, observable production services for heavy-equipment and construction environments.

Leads a scrum team developing AI-driven annotation automation for robotics, autonomy, computer vision, and VLA datasets. Designs and deploys automated labeling and captioning systems, trains and evaluates machine learning models, builds data pipelines incorporating human review and quality validation, integrates annotation providers, and improves service reliability and scalability. Provides hands-on technical leadership and mentorship while applying technologies such as NVIDIA Cosmos, computer vision, multimodal AI, and sensor-data workflows.

Designs and delivers modern data solutions, including on-premise systems, cloud migrations, data pipelines, feature stores, and vector stores supporting AI and generative AI products. Builds agentic AI integrations, ML data workflows, and production MLOps/LLMOps capabilities. Troubleshoots and optimizes data systems, implements process improvements, and communicates complex technical concepts to non-technical stakeholders.

Implement and maintain Immuta and Unity Catalog access-control policies, translating approved governance requirements into technical rules and configurations. Develop and execute tests for filtering, masking, entitlements, and policy behavior; troubleshoot authorization and platform issues; document controls, dependencies, evidence, and operational procedures. Collaborate with cloud operations, data engineering, security, IAM, privacy, governance, and business stakeholders while contributing reusable policy-engineering patterns and integration architecture.

Build and maintain scalable data pipelines and distributed processing solutions supporting analytics, reporting, and machine learning. Develop cloud-based Big Data platforms using Python, Spark, Hadoop, and AWS or GCP. Apply data quality, governance, monitoring, and automation practices; collaborate with architects, data scientists, and analytics teams; participate in Agile delivery and production support; and mentor junior engineers.

Architect and operate enterprise-scale data platforms supporting cybersecurity operations. Build batch and streaming pipelines, data lakes, warehouses, storage backbones, normalization frameworks, schemas, and telemetry integrations. Optimize scalability, reliability, cost, security, and data quality while enabling SIEM/XDR analytics. Collaborate with security operations, detection engineering, threat management, and data science teams, and maintain architecture documentation and operational runbooks.

Designs, develops, and supports scalable ETL pipelines and large-scale data platforms using Python, PySpark, SQL Server, SSIS, and Hadoop technologies. Responsibilities include database performance tuning, data modeling, production troubleshooting, batch scheduling, data-quality controls, CI/CD, secure development, and collaboration across technical teams. The role also involves mentoring developers and evaluating AI-assisted development tools.

Leads the design, development, optimization, and security of very large SQL Server databases and data warehouses. Oversees ETL and Microsoft BI pipeline development, data modeling, performance tuning, code reviews, and access management. Collaborates with DBAs, mentors developers, applies AI-assisted optimization, assesses risks, and drives scalable, reliable data products.

Design, build, and maintain ETL/ELT pipelines and API integrations to ingest, cleanse, transform, and analyze infrastructure and cloud data. Produce analytics, dashboards, and executive reports; automate workflows and support near-real-time processing. Follow SDLC/DevOps practices (Git, CI/CD), ensure data governance, quality, lineage, and support incident, release, and compliance activities.

Build and maintain Python-based data pipelines, ETL/ELT processes, SQL Server solutions, and business workflow automations. Develop AI-agent capabilities using Anthropic’s platform, integrate APIs, improve data quality and pipeline reliability, optimize database performance, and establish testing, monitoring, security, and governance standards. Collaborate with engineering, business intelligence, and company-wide teams to deliver scalable data solutions.

Lead design and implementation of large-scale data pipelines, ETL, and warehousing. Build and manage databases, ensure data quality/security, collaborate with analysts and scientists, evaluate new technologies, mentor junior engineers, and manage project delivery.

Designs, builds, and operates scalable enterprise data platforms supporting ingestion, processing, storage, governance, analytics, and AI/ML. Responsibilities include developing batch and real-time pipelines, lakehouse and warehouse architectures, orchestration, infrastructure automation, CI/CD, monitoring, security, cost optimization, and observability. The role partners with data, ML, DevOps, security, and application teams, troubleshoots complex platform issues, establishes reusable engineering standards, contributes to architecture strategy, and mentors engineers.

Provides technical leadership for enterprise data platform engineering. Designs and implements ETL, ELT, streaming ingestion, data models, cloud data lakes and warehouses, monitoring, data quality, governance, testing, version control, and CI/CD practices. Leads AWS, Snowflake, and dbt implementations while integrating disparate data sources for analysts and data scientists. Partners with business stakeholders to align data products with organizational strategy and enables self-service analytics and automation.

Design, build, and optimize relational database solutions and cloud-based data pipelines using SQL Server/Azure SQL, Databricks/Spark, Python/PySpark. Improve performance, support ETL/ELT, dimensional modeling, and AI/ML data needs. Collaborate with cross-functional teams, troubleshoot production issues, and adhere to governance and security standards.

Migrate data products from Azure Synapse to Microsoft Fabric and design scalable data pipelines using Azure Data Factory, notebooks, Spark/PySpark, Python, and SQL. Implement ingestion (full, delta, CDC), refactor pandas to PySpark, apply medallion architecture, perform data quality checks, tuning, monitoring, troubleshooting, and collaborate with stakeholders to document and deploy solutions.

Builds and operates scalable batch and real-time data pipelines (Databricks/Spark, Kafka), maintains Customer 360 data model and feature pipelines, ensures data quality/governance, delivers governed customer attributes to activation platforms, and supports privacy/compliance.

Design, architect, and deliver scalable data pipelines and storage solutions using big data and cloud technologies. Lead technical strategy, mentor engineers, ensure data governance, security, and performance, and collaborate with Data Science and Product teams to support analytics and ML initiatives.

Design, build, test, deploy, and monitor batch and streaming data pipelines and ETL processes. Evaluate and adopt data technologies (including Adobe Experience Platform and CDPs), collaborate with data scientists, architects, and business partners to ingest and prepare large semi-structured/unstructured datasets for ML training, model execution, and search analytics, and implement automation, documentation, and tooling for scalable analytic data systems.