Back
Mid-level data engineer role with common tech stack and metro context increases competition.
Core data engineering skills are broadly transferable across industries with low domain lock-in.
Explicit 5–7 years requirement and specific tech stack (PySpark, Snowflake, AWS) increases filter strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and deliver high-quality, production-ready data and software solutions using Python and PySpark in a platform-enabled environment.
Build scalable, reusable data products aligned with organizational standards, collaborating across systems, teams, and markets.
Engage with stakeholders to create robust technical designs and resolve complex production issues ensuring system reliability.
Bachelor’s degree in Computer Science, Software Engineering, or related analytical field (or equivalent practical experience).
5–7 years of professional software engineering experience with strong coding expertise.
Advanced proficiency in Python, PySpark, and ETL development; working knowledge of AWS, Airflow, Snowflake, Iceberg, SQL, Linux, Docker, GitHub, Terraform.
Strong understanding of data modeling concepts and multiple data storage systems; experience with Agile and Waterfall methodologies; test-driven development experience.
Experienced in building scalable, reusable data pipelines and data products that support multiple domains and markets.
Operates effectively within cross-functional, platform-driven teams focusing on standardized engineering practices and governance.
Skilled at translating business requirements into technical designs with a strong orientation towards long-term platform alignment and operational resilience.