Sr Manager - Data Architect

Organization:- Leading Global Management Consulting Organization

Role :- Senior Manager - Data Architect

Experience :- 12+ Years

What You'll Do

• Define architecture for key domains within the Data Products Portfolio.
• Evaluate data related tools and technologies and recommend appropriate implementation patterns and standard methodologies to ensure our Data ecosystem is always modern.
• Collaborate with Enterprise Data Architects in establishing and adhering to enterprise standards while also performing PoCs to ensure those standards are implemented.
• Provide technical expertise and mentorship to Data Engineers and Data Analysts in the Data & Solution Architecture.
• Develop and maintain processes, standards, policies, guidelines, and governance to ensure that a consistent framework and set of standards is applied across the company.
• Create and maintain conceptual / logical and physical data models to identify key business entities and visual relationships.
• Work with business and IT teams to understand data requirements.
• Maintain a data dictionary consisting of table and column definitions.
• Review Architecture with both technical and business audiences.
• Partner with Technology, Data Stewards and various Products teams in an Agile work stream while meeting program goals and deadlines.
• Champion architecture across the project lifecycle — working closely with Product Owners, Business, and BI&A teams as a trusted technical partner to shape and steer key decisions.
• Ability to put forward well-researched design approaches with clear pros and cons, evaluate and test new platform capabilities, and establish modern architectural ways of working across the team.
• Ability to craft & influcence well-structured proposals and present architectural recommendations persuasively, building stakeholder confidence and driving adoption of scalable, future-ready solutions.
• Lead to design / build new models to efficiently deliver the candidate and recuitment results to senior management.

What You'll Bring

Essential Education

• Bachelor's degree or equivalent combination of education and experience.
• Bachelor's degree in information science, data management, computer science or related field preferred.

Essential Experience & Job Requirements

• 12+ years of IT experience with major focus on data warehouse / database related projects.
• Expertise in cloud data platforms and databases like Snowflake, Redshift, BigQuery, Databricks, data catalog, MDM etc.
• Expertise in writing SQL and database procedures.
• Proficient in designing solution architecture factoring in all integration points, data flows and security/vulnerabilities.
• Proficient in Data Modelling - conceptual, logical, and physical modelling.
• Proficient in documenting all the architecture related work performed.
• Hands on experience in data storage, ETL / ELT and data analytics tools and technologies e.g., Talend, dbt, Attunity, Golden Gate, Fivetran, APIs, Tableau, Power BI, Alteryx etc.
• Experienced in Data Warehousing design / development and BI / Analytical systems.
• Experience working projects using Agile methodologies.
• Strong hands-on experience with data and analytics data architecture, solution design, and engineering experience.
• Experience with Cloud Big Data technologies such as AWS, Azure, GCP, Snowflake, BigQuery, and Databricks.
• Experience with Python would be preferable.
• Experience working with agile methodologies (Scrum, Kanban) and Meta Scrum with cross-functional teams (Product Owners, Scrum Master, Architects, and data SMEs).
• Review existing databases, data architecture, data models across multiple systems and propose architecture enhancements for cross compatibility and target systems.
• Excellent written, oral communication and presentation skills to present architecture, features, and solution recommendations.

GOOD TO HAVE: GENAI, AGENTIC AI & EMERGING ARCHITECTURE CAPABILITIES

These GenAI capabilities are optional / preferred and are captured separately from the core Data Architect requirements above.

• Hands-on experience designing, evaluating, or supporting GenAI / LLM-enabled solutions, including proofs of concept and production-oriented architecture patterns.
• Understanding of Agentic AI concepts, multi-step orchestration, tool-using AI agents, and human-in-the-loop design considerations.
• Experience with retrieval architectures such as RAG, GraphRAG, hybrid search, semantic search, and context-grounding approaches for enterprise use cases.
• Familiarity with vector databases, knowledge graphs, metadata-rich knowledge stores, and enterprise knowledge base design for AI applications.
• Exposure to AI application frameworks and orchestration tools such as LangChain and LangGraph.
• Awareness of agent-to-agent interaction patterns, A2A concepts, model context management (MCP), and emerging interoperability standards such as MCP.
• Experience evaluating data readiness for GenAI use cases, including chunking strategy, retrieval quality, data lineage, access controls, and content governance.
• Understanding of prompt design, evaluation approaches, observability, guardrails, safety, and responsible AI controls in enterprise environments.
• Ability to partner with product, engineering, architecture, and governance teams to shape scalable GenAI-enabled data products and platforms.