Data Platform Architect – Data & AI Practice

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data Platform Architect – Data & AI Practice based in India. Join a growing Data & AI practice and help design the next generation of enterprise data platforms that power advanced analytics and artificial intelligence. As a hands-on architect, you will lead the development of scalable, cloud-native data solutions, combining technical expertise with strategic advisory and engineering leadership. You will work with modern technologies such as AWS, Databricks, Snowflake, and generative AI frameworks to build secure, governed, and AI-ready data ecosystems. Partnering directly with North American customers, you will translate complex business challenges into practical architectures, prototypes, and modernization strategies. You will also guide engineering teams through implementation while establishing reusable architectural patterns, accelerators, and delivery standards. This is an opportunity to influence enterprise technology decisions, strengthen technical capabilities, and help organizations unlock measurable business value through data and AI innovation.

Accountabilities

• Enterprise Data Platform Architecture: Design and implement scalable, cloud-native data platforms covering data ingestion, integration, processing, lakehouse and warehouse architectures, analytics, governance, and AI enablement. Ensure architectural decisions support business objectives, security requirements, operational reliability, and long-term scalability.
• Cloud Architecture and Technology Strategy: Develop enterprise data solutions primarily on AWS while evaluating and leveraging Azure and Google Cloud Platform (GCP) capabilities where appropriate. Assess technology options, architectural trade-offs, and evolving cloud roadmaps to recommend solutions aligned with customer needs.
• AI-Ready Data Architecture: Design data integration patterns and platform capabilities that support generative AI applications, including retrieval-augmented generation (RAG), large language model (LLM) integration, agentic workflows, and Model Context Protocol (MCP)-based connectivity.
• Databricks and Snowflake Solution Design: Architect modern data solutions using Databricks and Snowflake, including lakehouse environments, data engineering pipelines, integration frameworks, and native AI capabilities. Define implementation approaches that optimize performance, maintainability, and cost efficiency.
• Technical Discovery and Prototyping: Lead technical discovery sessions, architecture workshops, proof-of-concepts (POCs), and minimum viable product (MVP) development. Translate business requirements into practical technical designs, validate architectural assumptions through hands-on experimentation, and guide teams toward production-ready solutions.
• Enterprise Data Modernization: Develop strategies for migrating and integrating legacy enterprise data systems into modern cloud platforms. Incorporate master data management (MDM), data quality, governance, and security practices to improve data consistency, accessibility, and reliability.
• Data Governance and Enterprise Integration: Define architectural approaches for data governance, quality management, security, and production operations. Evaluate and integrate enterprise data management platforms such as Reltio, Informatica, Collibra, and Alation into broader data ecosystems.
• Customer Advisory and Stakeholder Engagement: Build trusted relationships with North American customers, understand end-user needs, facilitate architecture discussions, and provide strategic recommendations. Communicate complex technical concepts clearly to business stakeholders, technical leaders, and delivery teams.
• Engineering Leadership and Delivery Support: Provide hands-on technical guidance throughout implementation, support engineering teams in resolving complex challenges, and ensure solutions align with architectural standards, business requirements, and operational expectations.
• Practice Development and Reusable Assets: Contribute to the growth of the Data & AI practice by developing reference architectures, reusable accelerators, implementation patterns, and technical frameworks. Support technology partnerships, client proposals, and solution development initiatives that strengthen delivery capabilities.

Requirements

• Professional Experience: Extensive experience d…