Data & AI Platform Engineer (Fabric / Databricks / Data Products)

We’re building AI capabilities that will transform one of the world’s largest industrial organizations from intelligent operations to next-generation industrial solutions.

Be part of this exciting journey where AI, Agentic AI, Data, Cloud, and innovation meet real-world impact at a global scale.

What makes this opportunity unique is the scale, complexity, and impact.

You will have the opportunity to:

• Build AI products and solutions that operate across a truly global enterprise.
• Work at the intersection of industry, engineering, business transformation, and technology.
• Collaborate with leading experts from ArcelorMittal and strategic technology partners.
• Help establish standards, platforms, governance, and operating models that will shape our future AI landscape.
• Convert bold ideas into production-grade solutions that create measurable business value.
• Contribute to one of the most ambitious AI transformation programs currently underway in the industrial sector.

We’re looking for passionate AI Engineers, Data Specialists, Architects, Cloud Engineers, and technology innovators who want to solve complex challenges, build solutions from the ground up, and shape what comes next.

🔥 Ready to take your AI career to the next level? Explore our open positions and apply now on LinkedIn. Come build the future of Industrial AI with ArcelorMittal!

Job Title – Data & AI Platform Engineer (Fabric / Databricks / Data Products)

Job Nature – Permanent

Work Location – Hyderabad

Experience – Overall 8-10 years experience. 5+ years in data engineering, of which 2+ years on Azure data platforms (Fabric, Synapse or Databricks) and 1+ year supporting AI / retrieval-augmented generation workloads

Purpose of the role

Build and operate the governed data and AI platform components – ingestion, transformation, Data Products, semantic models, integration services and shared AI services – that give Solution Pods trusted, reusable data and connectivity to SAP and enterprise systems; work alongside the partner and then operate the services.

Key Responsibilities

• Implement ingestion, transformation, storage, quality, lineage, metadata, access and semantic assets on Microsoft Fabric and the Aura/Databricks foundation.
• Build APIs, connectors, events and interfaces that expose governed Data Products and enterprise systems (SAP via approved interfaces, Power Platform SAP connectors, Azure API Management) to pods.
• Automate unit, integration, reconciliation, performance and data-quality testing; implement telemetry, alerting, failure handling, replay and support diagnostics.
• Contribute deployment automation through approved CI/CD paths with Platform Engineers.
• Implement data classification, access management and residency controls defined by the Data Governance Lead; ensure financial calculations run in SQL on governed data, never in a language model.
• Document design, ownership, known limitations, operating procedures and handover evidence; pair with partner engineers and take ownership of platform data assets.
• Operate shared data and integration services ArcelorMittal-led and support the solution pods.

Preferred Skills

• Stakeholder orientation
• Results orientation
• Courage to challenge
• Inspire trust
• Innovation and learning agility
• Decision making
• Cross-cultural collaboration
• Capability building and knowledge transfer

Good to have skills

• Strong SQL and Python (PySpark); data modelling and semantic-layer design (Power BI / Fabric)
• Data quality, reconciliation, lineage and metadata automation (Purview or equivalent)
• API / integration engineering: REST, Azure API Management, event-driven patterns, idempotency and retry
• Data engineering on Microsoft Fabric (Lakehouse, Data Factory, notebooks, semantic models) and/or Databricks (Delta, Spark, Unity Catalog)
• CI/CD for data assets and test automation
• Experience designing, building and operating reusable Data Products, domain-oriented datasets and governed semantic assets for enterprise consumption
• Strong understanding of lakehouse architecture, Delta Lake concepts, medallion architecture, data lifecycle management and large-scale enterprise data processing patterns
• Understanding of AI and Generative AI data foundations including document ingestion, chunking, metadata enrichment, vectorization, retrieval pipelines and grounding architectures
• SAP data extraction and integration (OData, CDS view…