Data Engineer Jobs in Irving, TX

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 a multidisciplinary data science and AI pod focused on AI-assisted engineering tools, adoption, analytics, and productivity measurement. Develops technical plans, guides deployment and troubleshooting, supports Agile product ownership, reports actionable insights, and mentors data science, machine learning, GenAI, and software engineering professionals. Partners with users, product owners, architects, engineers, and business stakeholders to shape scalable enterprise AI enablement services while applying responsible AI principles.

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.

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.

Lead design and build of scalable Azure-based data pipelines, lakehouse architectures, and optimized data models. Implement CI/CD, data governance, real-time streaming, and productionize ML feature pipelines while mentoring engineers and collaborating with analysts and business stakeholders to enable analytics and regulatory-compliant data operations.

Build and operationalize AI-enabled data products, agents, and reusable enterprise patterns using governed data platforms. Develop Snowflake and Databricks AI solutions, semantic grounding, retrieval-augmented generation, orchestration, guardrails, evaluations, observability, and production controls. Create reference architectures and implementation playbooks, evaluate emerging tools, and guide teams toward secure, scalable adoption. Partner with governance, risk, security, architecture, and data teams while mentoring practitioners and supporting responsible AI enablement across the enterprise.

Designs, builds, deploys, and operates scalable data pipelines and cloud-based data architectures supporting lending analytics across Mortgage and Personal Loans. Responsibilities include data ingestion and transformation, schema design, SQL optimization, workflow orchestration, monitoring, troubleshooting, data quality, and generative AI integration. The role also establishes engineering standards and collaborates with analysts, engineers, and governance teams to deliver reliable production data systems.

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.

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.

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.

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.

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.

As a Sr Data Engineer, you'll conduct feasibility analyses, develop applications, monitor processes, consult with users, and ensure compliance while serving as a mentor to junior analysts.

Designs and leads scalable real-time and batch data pipelines, data platforms, architectures, data products, and intelligent APIs. Ensures data quality, supports BI and AI/ML initiatives, and develops ingestion, processing, migration, and analytical solutions for big data, NoSQL, and data warehouse environments. Provides technical leadership, mentorship, code reviews, testing, CI/CD implementation, system monitoring, troubleshooting, and cross-functional translation of business requirements into reliable cloud-based solutions.

Lead SRE and production support for big data services: manage incident/problem management, monitoring/observability (AppDynamics, Splunk, ELK, Grafana), service improvement, automation (Ansible, CI/CD), disaster recovery, capacity/configuration/change management, and coordinate with development, architecture, and infrastructure teams while leading L2 service delivery and on-call support.

Provide production application and Big Data platform support for critical financial systems: monitor, troubleshoot, tune Hadoop/Cloudera clusters, manage incidents and releases, maintain runbooks, perform risk reconciliations, liaise with stakeholders and vendors, drive stability and SRE best practices, and coach junior staff.

Provide technical and business support for Citi applications, manage application environments, perform risk analysis, and lead enhancements while ensuring compliance with corporate standards.

Design, build, and support full-stack web applications and microservices to provide self-service test data capabilities. Develop Java/Spring Boot backend services and React front-ends, design RESTful APIs, automate test data provisioning, contribute to CI/CD pipelines, and provide production support and engineering best practices across teams.

The role involves design and development using Hadoop and its ecosystem, requiring strong programming skills in Java, Python, and SQL, with experience in data warehouses.