Global It Data Architect - Senior Manager

BCG BCG · Consulting · Gurgaon, Haryana, India · Technology and Engineering

This role is for a Senior Manager, Global IT Data Architect at Boston Consulting Group (BCG) in Gurgaon, India. The primary focus is on defining architecture for data products, evaluating and recommending data tools and technologies, and providing technical expertise to data engineers and analysts. The role involves creating data models, maintaining data dictionaries, and partnering with business and IT teams. While the core responsibilities are in data architecture and engineering, there is a 'GOOD TO HAVE' section mentioning optional/preferred experience with GenAI, Agentic AI, RAG, vector databases, and AI application frameworks, indicating an exploratory interest in these areas but not making them core to the role.

What you'd actually do

  1. Define architecture for key domains within the Data Products Portfolio.
  2. Evaluate data related tools and technologies and recommend appropriate implementation patterns and standard methodologies to ensure our Data ecosystem is always modern.
  3. Provide technical expertise and mentorship to Data Engineers and Data Analysts in the Data & Solution Architecture.
  4. Develop and maintain processes, standards, policies, guidelines, and governance to ensure that a consistent framework and set of standards is applied across the company.
  5. Lead to design / build new models to efficiently deliver the candidate and recuitment results to senior management.

Skills

Required

  • 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 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.

Nice to have

  • Bachelor's degree in information science, data management, computer science or related field preferred.
  • Experience with Python would be preferable.
  • Hands-on experience designing, evaluating, or supporting GenAI / LLM-enabled solutions, including proof 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 control