About this role
RESPONSIBILITIES:
Design, build, and operate scalable ELT pipelines using Python and PySpark, with a focus on reliability, performance, and maintainability
Own and improve batch and streaming data systems using Spark and Kafka, including monitoring and resolving production data issues
Develop and optimize Snowflake data models and DBT transformations to support analytics, experimentation, and trusted metrics
Partner with data scientists, analysts, and product teams to translate business requirements into well-designed data solutions
Contribute to the evolution of the data platform by improving observability, data quality, and engineering best practices
Leverage AI tools to accelerate development, improve code quality, and automate repetitive data engineering workflows
QUALIFICATIONS:
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience
3-5 years of professional experience building and operating ETL/ELT pipelines in production environments
Strong proficiency in SQL and hands-on experience with modern data warehousing concepts and dimensional modeling
Professional experience using Python for data engineering, including writing clean, testable, and reusable code
Experience with DBT for data modeling, testing, and documentation is preferred
Experience with Spark and Kafka for batch or streaming data processing is preferred
Strong problem-solving skills, clear communication, and the ability to work independently while collaborating in an agile environment
Comfort using AI tools such as Copilot or ChatGPT to improve efficiency throughout the software development lifecycle