ADF Fullstack Engineer

Навыки

• Agile
• AI-инструменты в работе
• Azure
• BI-инструменты
• Коммуникация
• Решения на данных
• Data Lake

О компании и продукте

• Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries.
• With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
• At Capgemini Mexico, we aim to attract the best talent and are committed to creating a diverse and inclusive work environment, so there is no discrimination based on race, sex, sexual orientation, gender identity or expression, or any other characteristic of a person. All applications are welcome and will be considered based on merit against the job and/or experience for the position.

Задачи

• As an ADF Fullstack Engineer at Capgemini , you will be responsible for designing and building scalable data solutions in a cloud-first environment using Microsoft Azure technologies
• You will combine software engineering, data architecture, and ETL expertise to deliver high-quality data pipelines and analytics solutions that support business decision-making
• Responsibilities & Scope
• Design, develop, and maintain data pipelines using Azure Data Factory (ADF)
• Write efficient SQL/T-SQL queries for data extraction, transformation, and loading (ETL)
• Develop and optimize data models in Azure SQL Server and Azure Synapse Analytics
• Implement data partitioning strategies and performance tuning for large-scale data environments
• Collaborate with cross-functional teams to translate business requirements into technical solutions
• Participate in Agile ceremonies including sprint planning, stand-ups, and retrospectives
• Build and maintain dashboards and reports using Power BI
• Ensure data quality, integrity, and security across platforms
• Automate data workflows and monitor pipeline performance and reliability
• Document technical solutions and manage version control using Git
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Требования

• Data-driven mindset – Passionate about building scalable and reliable data solutions
• Cloud-focused engineer – Strong experience working with Azure-based data platforms
• Problem solver – Translates complex business needs into efficient data architectures
• Collaborative professional – Works effectively within Agile, cross-functional teams
• Quality-oriented – Ensures accuracy, performance, and security in all data processes
• Qualifications & Preferred Skills
• 4–6 years of experience in data engineering / ETL development
• Strong hands-on experience with Azure Data Factory (ADF)
• Proficiency in SQL Server, Azure SQL Database, and Azure Data Lake
• Solid understanding of data warehousing concepts , including partitioning and performance optimization
• Experience with Azure Synapse Analytics
• Experience building reports and dashboards using Power BI
• Familiarity with Agile methodologies and DevOps practices
• Strong problem-solving and communication skills
• Good-to-Have Certifications
• Microsoft Certified: Azure Data Engineer Associate
• Microsoft Certified: Azure Solutions Architect Expert
• Microsoft Certified: Power BI Data Analyst Associate
• DP-203: Data Engineering on Microsoft Azure
• DP-500: Designing and Implementing Enterprise-Scale Analytics Solutions
• Job Description
• Data engineers are responsible for building reliable and scalable data infrastructure that enables organizations to derive meaningful insights, make data-driven decisions, and unlock the value of their data assets
• Job Description - Grade Specific
• The primary focus is to help organizations design, develop, and optimize their data infrastructure and systems
• They help organizations enhance data processes, and leverage data effectively to drive business outcomes

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