About this role
Lead the development, configuration, and maintenance of application development platforms. Ensure platforms are scalable, secure, and efficient. Work closely with application developers to optimize environments for development and deployment. Mentor junior team members and provide technical guidance.
• 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.