Data Engineer (Senior) - ETL (Python+Snowflake) - Remote, Latin América

Bluelight is a leading software consultancy dedicated to designing and developing innovative technology that enhances users' lives. With a steadfast commitment to delivering exceptional service to our clients, Bluelight excels in its focus on quality and customer satisfaction. Our mission is not only to create cutting-edge applications but also to foster a collaborative and enriching work environment where each team member can grow and thrive. With a presence across the United States and Central/South America, Bluelight is in an exciting phase of expansion, continually seeking exceptional talent to join its dynamic and diverse community. We are looking for a skilled individual to join our rapidly growing team at Bluelight. This position is ideal for someone who thrives in a fast-paced, dynamic environment where everyone's opinions and efforts are valued and appreciated. You will have the opportunity to contribute to challenging and meaningful projects, developing high-quality applications that stand out in the market. We value continuous learning, personal growth, and hard work, offering a collaborative environment that promotes professional development. If you are passionate about software development and eager to be part of a growing software consultancy, we invite you to apply and join us on this exciting journey. What we are looking for Education & Experience Education: Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related field (or equivalent professional experience). Core Experience: Proven experience developing end-to-end data pipelines extracting/transforming/loading data from REST APIs, relational databases, cloud storage, and flat files. Snowflake Proficiency: Demonstrated hands-on experience with virtual warehouses, streams, tasks, stages, Snowpipe, secure data sharing, and performance optimization. SQL: Advanced SQL development skills with the ability to write complex queries, tune performance, and optimize large-s