Global Data Architect

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.