Data Architect - Snowflake

Do you want to boost your career and collaborate with expert, talented colleagues to solve and deliver against our clients' most important challenges? We are growing and are looking for people to join our team. You'll be part of an entrepreneurial, high-growth environment of 300.000 employees. Our dynamic organization allows you to work across functional business pillars, contributing your ideas, experiences, diverse thinking, and a strong mindset. Are you ready?

Job Overview:

Infosys Consulting is at the forefront of applied AI innovation, delivering real-world business value through the convergence of AI agents, machine learning, and modern enterprise architecture. As part of our growing Enterprise AI consulting practice, we are looking for technically hands-on professionals to design and deliver client-centric intelligent systems and support business growth through strategic pre-sales and solutioning initiatives.

You will act as the primary data architecture liaison between business stakeholders and IT delivery teams. Lead solution architecture and design across enterprise data domains including product and material master data, commercial and sales, customer and consumer, manufacturing, supply chain, finance, procurement, ERP and PLM. 
Key Responsibilities
- Lead business-to-technology architecture discussions and convert requirements into scalable solution designs. 

- Design and govern enterprise-scale data architectures using Snowflake as the strategic cloud data platform. 

- Define standards for data modelling, incremental processing, change data capture, performance optimisation, security and access control. 

- Define scalable, observable and maintainable ingestion, processing and data-product patterns. 

- Establish reconciliation controls covering completeness, accuracy, timeliness and consistency between source systems and Snowflake. 

- Define semantic-layer standards for Power BI, Tableau or equivalent analytics platforms and ensure KPI-to-source traceability. 

- Identify technical dependencies, architecture risks, resource needs and implementation constraints. 

- Support data foundations for AI and advanced analytics use cases.