About The Role

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.