Data Engineer - Reinvention Centers

Accenture•Dallas, TX

19h•Hybrid

About The Position

The Advanced Technology Centers (ATCs) are the engine for reinvention in our clients’ transformation journey. Powered by more than 255,000 people across 24 countries, ATCs provide our clients with seamless access to industry insights and innovative technology solutions. Stronger together! The Advanced Technology Centers (ATCs) make a tremendous impact in solving our clients’ business problems by leveraging innovation, intelligence, industry insights, new IT, and new technology skills. With the global environment changing at a faster pace, our clients are facing unprecedented challenges, and they need us more than ever before. As a Network, ATCs are positioned to unlock greater opportunities and exponential value for our clients. The value for our clients and our people: For our clients, the Network provides the strength of our geographic diversity, greater resilience, and seamless access to the deepest industry knowledge, the latest in Gen AI solutions, and tech expertise from around the world. For our people, it brings an opportunity to shape truly boundaryless career paths in a highly collaborative team of experts where they can learn from each other and solve the world’s most complex client challenges. You Are A data engineer with strong expertise in Azure, focused on designing, building, and operating scalable data pipelines and AI-powered solutions, ensuring reliable, high-quality data delivery in enterprise environments. The Work You embed with clients to design, build, and optimize enterprise-grade data pipelines and AI-powered workflows, with a primary focus on Microsoft Azure. Leveraging Azure Data Factory, Azure Databricks, and Azure AI Foundry, you partner with stakeholders to define data engineering use cases, prototype scalable architectures, and deliver production-ready solutions that operate reliably across hybrid and cloud environments. You also support day-to-day data engineering operations — monitoring pipeline health, managing incidents, and ensuring data systems run smoothly in production.

Requirements

Minimum of 2 years experience designing, building, and optimising scalable data pipelines using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics, or equivalent services on AWS or Google Cloud, in production environments.
Minimum of 2 years experience with data modelling and warehousing — including schema design, ETL/ELT development, and Azure SQL or equivalent cloud database services — to support high-volume, complex data workflows.
Minimum of 1 year experience in data pipeline operations — monitoring performance, triaging failures, managing incidents, and maintaining SLAs across production cloud data environments on Azure, AWS, or Google Cloud.
Minimum of 1 year experience integrating data pipelines with enterprise APIs, microservices, and workflow systems, and deploying via CI/CD on Azure DevOps, AWS CodePipeline, or equivalent cloud DevOps tooling.
Minimum of 1 year experience programming in Python or PySpark for data engineering, including scripting, transformation logic, and distributed data processing on cloud platforms such as Azure, AWS, or Google Cloud.
Minimum of 1 year experience ensuring data quality, lineage, and governance — including validation frameworks, monitoring, and adherence to data standards across cloud data platforms.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate Degree, must have minimum 6 years work experience)

Nice To Haves

Bachelor's degree in Computer Science, Data Engineering, or equivalent field

Responsibilities

Lead the design, development, and optimization of scalable data pipelines on Azure — using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics — to support complex, high-volume data workflows.
Support data engineering operations by monitoring pipeline performance, triaging failures, and maintaining SLAs across production data environments.
Provide technical guidance and mentorship to team members on Azure data engineering best practices to enhance overall project delivery.
Collaborate with cross-functional teams to align Azure-based data engineering solutions with broader business objectives and data strategy.
Analyze and troubleshoot data processing issues across Azure pipelines to ensure high data quality, performance, and system reliability.
Contribute to the continuous improvement of data architecture standards, engineering best practices, and Azure platform governance.
Assist in operationalizing data pipelines — including scheduling, alerting, logging, and incident response — to ensure reliable and consistent data delivery.
Work with key business representatives, data owners, end users, application designers, and data architects to model current and future data landscapes on Azure.
Design and implement data models in Azure Synapse Analytics and Azure SQL that meet business requirements and enhance data usability and accessibility.
Continuously evaluate and improve Azure data architecture processes to enhance pipeline efficiency, cost optimization, and operational effectiveness.
Design and integrate AI agents using Azure AI Foundry — including retrieval, orchestration, tool invocation, evaluation harnesses, and lifecycle observability — into enterprise data workflows.
Integrate AI agents and data pipelines with enterprise APIs, microservices, and workflow systems; ship and maintain via CI/CD on Azure DevOps.