Data Platform Architecture – Senior Lead Role Summary

Own and lead the end‑to‑end architecture of the Target State x Data Platform and Strategic agentic AI solution, ensuring scalable, secure, and interoperable data capabilities across ingestion, storage, processing, and consumption layers. Provide architectural direction and governance for platform and data engineering teams, enabling consistent adoption of open Lakehouse architecture based on Apache Iceberg,Databricks and Snowflake to support Core Reference, IBOR, Datahub, and Stamford Data Warehouse and many other data initiatives. Meanwhile, build Agentic AI solutions/platform to automate data operations related to investment performance, investment research, sales, client coverage, product, and related daily operations.

Key Responsibilities

• Define and own the end-to-end reference architecture for the x Data Platform, covering ingestion, Lakehouse storage, metadata, compute engines, and consumption patterns
• Establish architectural standards and guardrails for Iceberg based Lakehouse design, including table layout, partitioning, schema evolution, and lifecycle management
• Drive multi‑engine interoperability across Databricks , Snowflake and other data computing engines, ensuring consistent and governed access to shared data assets
• Build agentic AI solutions to automate data operations related to investment performance, investment research, and related daily operations.
• Review platform and data engineering designs to ensure alignment with architectural principles, performance expectations, and enterprise standards
• Partner with Platform Engineering to align infrastructure, security, observability, and CI/CD capabilities with architectural intent
• Partner with Data Engineering teams to ensure data pipelines, models, and data products conform to approved architectural patterns
• Work with enterprise Architecture Governance and Security teams to ensure adherence to access control, compliance, and audit requirements
• Publish and maintain architectural artifacts, including reference diagrams, design guidelines, and architectural decision records
• Identify architectural risks, technical debt, and improvement opportunities, guiding platform evolution over time

Qualifications

• Proven experience owning or leading enterprise scale data platform or agentic AI architecture
• Demonstrated ability to operate as an AI-augmented architect, leveraging GenAI tools to accelerate solution design, documentation, and decision-making while applying strong architectural judgment, governance, and quality standards.
• Strong architectural experience with Databricks or Snowflake
• Strong understanding of distributed data processing, scalability, and performance considerations
• Experience architecting data platforms and agentic AI solutions in AWS,GCP and/or Azure environments
• Solid understanding of data governance, metadata management, and security architecture
• Ability to guide and influence multiple engineering teams through architectural leadership
• Experience working in large-scale or regulated enterprise environments preferred
• Financial or enterprise data platform background preferred but not required

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