Data Platform Architect – Data & AI Practice

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 designing and implementing enterprise data platforms, with a strong background in hands-on architecture, solution engineering, and technical leadership. Cloud Platform Expertise: Proven experience architecting enterprise data solutions on AWS, alongside a solid understanding of Azure and GCP architectures, capabilities, technology roadmaps, and comparative trade-offs. Databricks and Snowflake: Deep expertise in both platforms, including lakehouse architecture, data engineering, pipeline development, integration patterns, and native AI capabilities. Candidates should be comfortable making architectural decisions and guiding implementation across these technologies. Generative AI Architecture: Working knowledge of modern AI-enabled data architectures, including RAG, LLM integration, agentic AI workflows, and MCP-based connectivity. Ability to design data foundations that support secure, reliable, and scalable AI applications. Enterprise Data Management: Strong understanding of master data management, data quality, governance, security, and production operations. Familiarity with enterprise platforms such as Reltio, Informatica, Collibra, and Alation, including their role within integrated data architectures. Architecture and Engineering Skills: Ability to translate complex business requirements into scalable technical designs, build working prototypes, evaluate architectural alternatives, and provide hands-on guidance throughout implementation. North American Customer Experience: At least five years of direct experience working with North American customers, including technical discovery, architecture workshops, solution advisory, stakeholder management, and client-facing technical discussions. Communication and Consulting: Excellent verbal and written communication skills, with the ability to explain technical concepts, present architectural recommendations, influence stakeholders, and build trusted customer relationships. Strategic and Analytical Thinking: Strong problem-solving abilities, sound technical judgment, and a customer-focused mindset. Able to balance immediate business needs with long-term platform scalability, governance, and maintainability. Leadership and Collaboration: Experience guiding engineering teams, facilitating technical discussions, and aligning cross-functional stakeholders around architectural decisions and delivery priorities. Preferred Qualifications: Experience with enterprise modernization programs, AI-ready data platform implementations, technology partnerships, consulting engagements, or building and scaling a technical practice. Benefits Permanent Employment: A full-time, permanent opportunity within a growing Data & AI practice. Remote Work: Remote working arrangement based in India, with Hyderabad identified as the associated location. Strategic Technical Influence: The opportunity to shape enterprise data architecture, influence technology decisions, and establish modern data and AI implementation standards. Hands-On Innovation: Work with leading cloud data platforms and emerging AI technologies, including AWS, Databricks, Snowflake, and generative AI architectures. Customer Advisory Exposure: Collaborate directly with North American customers on complex enterprise challenges, modernization initiatives, and long-term technology strategies. Practice-Building Opportunities: Contribute to reusable architectures, accelerators, technical partnerships, and solution development that support the growth of a Data & AI practice. Professional Development: Expand expertise across cloud ecosystems, enterprise data management, and emerging AI capabilities while helping guide engineering teams. Compensation and Additional Benefits: Salary, healthcare coverage, paid leave, and other benefits were not specified in the original job description and can be clarified during the recruitment process.