AI Data Engineer

Who we are

Join our global Centre of Excellence as a subject matter expert (SME) for Agentic AI Services, collaborating with teams worldwide to drive innovation and excellence. As part of this central function, you will help shape, implement, and scale cutting-edge Agentic AI solutions across the organization and our clients

This hands-on role requires deep expertise in artificial intelligence, machine learning, and data to design, build, and deploy advanced Agentic AI-driven solutions tailored to clients' unique needs.

The Data Engineer is a highly skilled and deep subject matter expert responsible for architecting, developing, and optimizing data pipelines and transformations that prepare data for analysis and consumption by Agentic AI systems. This expert plays a pivotal role in delivering high-quality, organized, and reusable datasets through scalable data engineering practices, leveraging Microsoft Fabric and the broader Microsoft ecosystem to create a modern, AI-ready data environment.

This role analyzes diverse source systems and data structures, building robust data models and pipelines to support analytics, data science, and operational intelligence as the business evolves towards Agentic AI capabilities. Additionally, this role provides guidance, coaching, and mentorship to other data engineers and cross-functional teams.

What you'll be doing

• Leads the design, implementation, and optimization of data pipelines using Microsoft Fabric, Azure Data Factory, Dataflows, and related Microsoft tools to ingest, transform, and deliver data fit for Agentic AI use-cases.
• Develops, automates, and maintains robust ETL/ELT processes that extract data from various sources—including on-premises and cloud—and integrate them into unified, structured formats within enterprise data lakes and warehouses.
• Analyzes business and technical requirements to design and develop scalable data models and data products supporting real-time, operational, and predictive analytics for Agentic AI adoption.
• Participates in translating conceptual and logical data models into efficient physical database schemas using Microsoft SQL, Azure Synapse, Microsoft Fabric Lakehouses, and related platforms, ensuring adherence to best practices and performance standards.
• Oversees migration and transformation of data between disparate systems (e.g., SAP, Oracle, Dynamics, SQL databases), ensuring secure, accurate, and reliable data provisioning for downstream AI workloads.
• Collaborates with business analysts, data architects, and AI/ML engineers to ensure seamless delivery of data assets optimized for advanced analytics and Agentic AI workflows.
• Defines, conducts, and oversees rigorous data validation and quality assurance processes, including the creation and execution of unit and integration test scenarios.
• Develops and maintains documentation for data pipelines, models, transformations, and migration activities, ensuring transparency and reproducibility.
• Advises on and implements data governance, metadata management, and lineage tracing in Microsoft Purview and Fabric, supporting compliance, discoverability, and readiness for AI.
• Identifies opportunities to automate data ingestion, transformation, and quality assurance tasks using scripting and modern orchestration practices.
• Coaches and mentors less experienced data engineers and collaborates across multidisciplinary teams to foster a culture of modern data engineering and readiness for Agentic AI enablement.

What you'll bring along

• Bachelor’s degree or equivalent in Computer Science, Software Engineering, Information Technology, or a related quantitative/engineering field.
• Minimum 5-10 years of experience designing, building, and optimizing modern data solutions in Microsoft Azure, especially with Microsoft Fabric, Data Factory, and Synapse.
• Relevant Microsoft certifications (e.g., Azure Data Engineer Associate, Microsoft Certified: Fabric Analytics Engineer) are highly regarded.
• Other related certifications in data engineering platforms/tools are a pl
• Deep expertise in data pipeline creation, transformation, and automation using Microsoft Fabric, Azure Data Factory, Power Query, Synapse Analytics, and Microsoft SQL-based solutions.
• Excellent understanding of modern data engineering practices, including data lakehouse architectures, ETL/ELT design, and data modeling in the context of preparing data for AI systems.
• Strong grasp of both logical and physical data modeling concepts, with experience implementing schemas that optimize data utilization by analytics and AI platforms.
• Analytical mindset with robust problem-solving abilities, capable of translating business requirements into scalable data engineering solutions.
• Demonstrated programming skills in SQL, Python, PowerShell, and (where relevant) Dataflow/Spark languages within the Microsoft data engineering ecosystem.
• Familiarity with data governance, lineage, and quality assurance practices in Microsoft Purview and related Microsoft tools.
• Expertise with multiple database types, including Azure SQL, Fabric Lakehouse, Azure Data Lake, and third-party sources (SAP, Oracle, etc.).
• Experience with real-time and batch data processing, scaling data pipelines to support high-volume/complex analytics requirements.
• Skilled in mentoring and guiding less experienced team members to adopt best-in-class cloud data engineering and tools.
• Demonstrated experience in ingesting, transforming, and structuring data from large and complex, multi-terabyte data sources for analytics and AI/ML applications.
• Proven success in automating and scripting data pipelines (Python, PowerShell, SQL, etc.) for scalable, repeatable ingestion and transformation both on-premises and in cloud.
• Experience with various database and data warehouse platforms (Azure SQL, Fabric Lakehouse, SAP, Oracle, etc.) and integrating, migrating, and validating data across applications.
• Familiarity with big data tools (Hadoop, Spark) as integrated within the Microsoft Azure ecosystem is preferred.
• Experience with end-user tools such as Power BI and Excel (pivots, macros) and supporting quality assurance for downstream reporting and analytics.
• Track record of supporting enterprise migration to AI-driven analytics solutions and mentoring teams in adopting modern Microsoft data engineering practices.
• Excellent command of both spoken and written English.