This role involves building data pipelines, migrating legacy architecture to AI ready data & semantic layer, collaborating with stakeholders, and optimizing data workflows to support advanced analytics and business intelligence initiatives.

Key Responsibilities

• Design, build, and maintain robust, scalable, and reliable data pipelines to support analytics, reporting, and AI/ML use cases
• Lead the migration of legacy data architectures to modern, cloud‑based, AI‑ready data platforms
• Develop and implement semantic layers to enable consistent, business‑friendly access to data for analytics and BI tools
• Collaborate closely with business stakeholders, data scientists, analysts, and product teams to understand data requirements and translate them into technical solutions
• Ensure data quality, integrity, security, and compliance through validation, monitoring, and governance best practices
• Document data architectures, pipelines, and best practices to support knowledge sharing and long‑term maintainability

• Bachelors / Master’s degree in Economics, Statistics, Engineering or a related quantitative field from a top-tier university
• Advanced Python & Pyspark: 2+ years of hands-on experience with performance tuning and complex query optimization
• Hands‑on experience with modern data architectures such as data lakes, lakehouses, and data warehouses such as Snowflake, Databricks, BigQuery, or Redshift
• Snowflake certification (SnowPro Core required; SnowPro Advanced preferred)
• Excellent communication skills, with the ability to explain technical concepts to non‑technical audiences

Desirable Skills (Good-to-Have)

• Experience supporting AI/ML pipelines and feature engineering
• Working knowledge of data visualization software such as Tableau, Power BI and Looker
• Hands-on experience with cloud platforms: AWS, Azure, or GCP.