Data Engineer Jobs in Irving, TX

Design, build, and maintain optimized, highly available data pipelines and the BMG data warehouse. Lead medium-to-complex data engineering initiatives, perform solution design, code reviews, QA, data integration, and collaborate across teams. Support cloud migration and ensure compliance with data policies for reporting and analytics.

Lead data engineering initiatives by designing scalable data pipelines, services, architectures, models, and structures using Hadoop, Ab Initio, Exadata, SQL, and cloud platforms. Analyze and troubleshoot complex data in warehouse environments, support risk management and business intelligence, resolve production and data integrity issues, gather requirements, lead designs, and mentor engineering staff throughout the project lifecycle.

Lead and audit optical infrastructure work in customer data centers and co-locations, including fiber installation, migrations, remediation, decommissioning, break/fix, remote hands, testing, and troubleshooting. Interact with customers and vendors, provide service updates, interpret procedures and network documentation, and ensure quality, safety, accurate cabling, and operational standards. The role requires hands-on fiber expertise, customer communication, physical equipment handling, and travel to non-home-market sites for extended periods.

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.

Design, develop, and support Denodo virtual views and data services; integrate Denodo with databases, APIs and cloud platforms; build reusable semantic/logical layers; optimize queries and performance; collaborate with architects and business teams; document designs, test, review code, deploy, and provide production support.

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.

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.

Lead cloud and data platform engineering for analytics and AI: implement DevSecOps CI/CD pipelines, automate infrastructure with IaC, manage Snowflake data warehouse and ingestion, oversee cloud cost reporting and FinOps, and evaluate/operate emerging AI/LLM capabilities and governance. Act as senior individual contributor collaborating with security, finance, and engineering teams to optimize performance, security, and cost.

Build, optimize, and operate the MIR Data Platform using high-performance relational data models, metadata-driven schemas, event-driven processing, reporting structures, and audit capabilities. Troubleshoot performance issues, blocking, deadlocks, and asynchronous processing failures. Support cloud migration to managed SQL platforms, production readiness, monitoring, HA/DR, incident resolution, and on-call coverage while collaborating with DBAs, SREs, architects, and platform teams.

Design, build, and optimize large-scale ETL/ELT pipelines and lakehouse architectures on cloud platforms using Databricks, Spark, Delta Lake, and Snowflake. Lead data federation with Starburst, improve performance and governance, support AI/ML projects (RAG/Agentic AI), mentor engineers, manage stakeholders, and deliver solutions in an Agile environment.

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.

Lead design and implementation of scalable cloud data platforms and ETL/ELT pipelines using PySpark/Databricks, Delta Lake, and Snowflake. Optimize Spark workloads, enforce data governance, support AI/ML (including RAG and Agentic AI), manage data federation with Starburst, mentor engineers, and engage stakeholders in an Agile environment to deliver enterprise-scale analytics solutions.

Build, implement, and support enterprise data and ETL systems across the full development lifecycle. Conduct systems analysis, design data warehouse and cloud solutions, provide operational support, manage risks, and implement security controls. Collaborate with business and technology stakeholders, resolve complex technical issues, establish development standards, and mentor junior analysts. The role requires expertise in AbInitio, AWS orchestration tools, multiple data platforms, Python, PySpark, Unix scripting, and banking technology.

Design, build, and maintain scalable cloud-native data pipelines, microservices, and platforms to support real-time and batch processing. Collaborate with architects and stakeholders to translate requirements, implement AWS-based ingestion/streaming solutions, enforce data quality/testing, and operate observability and CI/CD practices to ensure reliable production data systems for autonomy initiatives.

Design, build, and maintain enterprise analytics solutions and semantic data models using Microsoft Fabric, Databricks, SQL, Python, and BI tools. Deliver scalable reporting datasets, executive dashboards, data quality standards, and CI/CD practices. Lead architecture discussions, mentor engineers, collaborate with stakeholders, and drive analytics strategy to enable data-driven decisions.

Design, build, and support an AI-ready enterprise data enablement platform using Databricks, dbt, Delta Lake, and Azure data services. Create reusable CI/CD and IaC accelerators, govern data products via Unity Catalog, enable analytical access with Starburst/Trino, troubleshoot production issues, and collaborate with cross-functional teams to deliver governed, reusable, and production-grade data products for analytics and GenAI use cases.

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.

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.

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.

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.

Lead development and mentor junior engineers to design, build, and maintain scalable data and integration solutions. Implement Python/SQL-based services, REST/JSON APIs, Azure cloud deployments, CI/CD and DevOps practices, Power BI reporting, and enterprise ETL/data orchestration to support reliable production environments and business intelligence.