Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline
2+ years of hands-on experience in data engineering, ETL development, cloud-based data solutions , or building data products that serve analytics, automation, or machine learning needs
Strong foundational knowledge of AWS cloud services, including S3, Lambda, Glue, and Snowflake, with a focus on scalable and cost-efficient data architectures
Proficiency in Python, with experience in modularization, writing optimized, production-ready code for data transformations and automation
Advanced SQL skills, including query optimization, performance tuning, and database design
Experience in building robust and scalable data pipelines using AWS Glue, Step Functions and SQL-based transformations (using stored procedures)
Solid understanding of data modeling, data warehousing concepts, and schema design best practices
Hands-on experience with Tableau or other BI tools for data visualization and dashboard development
Exposure to DevOps and CI/CD practices, including infrastructure-as-code, version control (Git), and automated deployment strategies
Strong problem-solving mindset, with the ability to troubleshoot and optimize complex data workflows efficiently
Excellent communication and collaboration skills, with the ability to work effectively in agile, cross-functional teams
Experience with Databricks for scalable data processing and PySpark for distributed data transformations (preferred)
