Join a collaborative team where you’ll build reliable data solutions that power analytics and business decisions. In this role, you’ll work hands-on with Python and ETL to design, develop, and optimize data pipelines that move and transform data across systems with accuracy and speed. You’ll partner closely with analysts, data engineers, and stakeholders to understand requirements, improve data quality, and deliver scalable solutions. If you enjoy solving data challenges, writing clean and maintainable code, and continuously improving how data flows through an organization, this is a great opportunity to grow your impact while working in a supportive, learning-focused environment. Develop and maintain ETL pipelines using Python to extract, transform, and load data from multiple sources. • Write efficient SQL queries for data extraction, validation, reconciliation, and performance tuning. • Implement data cleansing, transformation logic, and business rules to ensure high-quality datasets. • Collaborate with stakeholders to gather requirements and translate them into technical specifications and workflows. • Monitor pipeline execution, troubleshoot failures, perform root-cause analysis, and implement preventive fixes. • Create and maintain technical documentation for ETL jobs, data mappings, and operational runbooks. • Follow coding standards, version control practices, and participate in code reviews to ensure maintainability. • Support testing activities by preparing test data, validating outputs, and ensuring end-to-end data integrity. Minimum Qualifications: • Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. • 2–5 years of experience in Python development and ETL/data integration work. • Strong working knowledge of SQL, including joins, aggregations, subqueries, and query optimization basics. • Experience handling structured datasets and implementing transformation/validation logic. • Ability to troubleshoot data issues and communicate findings clearly to technical and non-technical teams. Preferred Qualifications: • Experience with orchestration/scheduling tools (e.g., Airflow) and building dependable batch workflows. • Familiarity with Python data libraries (e.g., Pandas) for transformations and data quality checks. • Exposure to cloud data platforms or warehouses (e.g., AWS/GCP/Azure, Snowflake/Redshift/BigQuery). • Experience with CI/CD practices and automated testing for data pipelines. • Demonstrated ability to improve pipeline performance, reliability, and observability through monitoring and logging.