Data Engineer Snowflake

Job description

Job Description: 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. Responsibilities: 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 - Qualifications: 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.