Data Architect (Onsite)

Required Skills

• 1 3 yrs of exp Revenue Data Platform
• 2 3 yrs of exp Azure, Snowflake
• 3 3 yrs of exp Databricks
• 4 3 yrs of exp AWS

Role Overview

• We are seeking a highly skilled Data Architect to lead the design and evolution of our Revenue Data Platform (RDMP). This role will be instrumental in architecting a scalable, enterprise-grade data platform that enables unified data access and delivers actionable insights across Client business units.
• The ideal candidate will bring a forward-looking mindset, translating strategic vision into practical architecture, while driving innovation, cost efficiency, and measurable business outcomes.

Key Responsibilities

• Define and architect a scalable, enterprise-wide Revenue Data Platform to support diverse business needs Translate business vision into robust data architecture, design patterns, and implementation roadmap Develop proof of concepts (POCs) to validate architectural decisions and accelerate adoption
• Design solutions leveraging modern data platforms including Azure, Snowflake, Databricks, and AWS Collaborate closely with engineering teams to ensure hands-on alignment and execution Incorporate cost optimization strategies into architecture and platform design Deliver incremental value aligned to specific business units through phased implementation Ensure data solutions enable high-quality, secure, and governed data access across the enterprise

Required Qualifications

• Proven experience as a Data Architect designing and implementing large-scale data platforms Strong expertise in cloud data ecosystems (Azure preferred; AWS experience is a plus) Hands-on experience with Snowflake,
• Databricks, and modern data engineering practices
• Experience working with CI/CD tools such as GitLab Ability to bridge strategic architecture and hands-on engineering execution
• Strong understanding of data modeling, data governance, and data platform scalability Preferred Qualifications Experience in Finance/Revenue domains Familiarity with enterprise platforms such as SAP and Oracle Exposure to AI/LLM tools (e.g., Claude) and their integration into data platforms