What makes you a great fit:

• Design and implement robust data pipelines and ETL processes using Python, PySpark, and AWS Glue.
• Develop and maintain data lake solutions, ensuring scalability, security, and performance.
• Implement and manage data lineage and metadata management processes. 
• Collaborate with data scientists, analysts, and stakeholders to understand data requirements and translate them into technical solutions. Optimize data storage and retrieval for efficiency and cost-effectiveness using technologies such as Snowflake.
• Ensure data quality through data validation, testing, and monitoring. Implement data governance policies and procedures to ensure compliance and security.
• Perform data analysis and troubleshooting to resolve data-related issues.
• Design and develop data visualizations and dashboards to enable data-driven decision-making.
• Stay updated with industry trends and best practices in data engineering and analytics.
• Proven work experience as a Data Engineer or similar role.
• Strong proficiency in Python programming and experience with PySpark for big data processing.
• Hands-on experience with AWS services including Glue, S3, EMR, and Lambda.
• Solid understanding of data lake architecture and design principles.
• Experience with data lineage tools and metadata management.
• Familiarity with Snowflake or similar cloud data warehouse platforms.
• Proficiency in SQL and database technologies (relational and non-relational databases).
• Experience with data visualization tools such as Tableau, Power BI, or equivalent.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork skills.
• Master’s degree in computer science, Data Engineering, Data Science, or a related field.
• Certification in AWS or related cloud platforms.
• Experience with DevOps practices and CI/CD pipelines.
• Knowledge of machine learning concepts and frameworks.
• Experience with Agile/Scrum methodologies.