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Mid-level popular data role with broad tech requirements at a known multinational increases competition.
Data engineering skills transfer across industries but require platform and tooling knowledge.
Explicit 5–7 years plus mandatory tech/data-platform skills and TDD make filters strict.
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Design, develop, test, and deploy production-ready data pipelines and applications using Python, PySpark, and related technologies in a platform-enabled environment.
Build scalable and reusable data solutions that align with organizational standards, and collaborate with cross-functional teams to translate business requirements into technical designs.
Debug complex production issues, contribute to engineering best practices, and work with platform capabilities for data storage, orchestration, and governance.
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 hands-on 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 software development methodologies.
Experienced in building platform-oriented, reusable data engineering solutions across multiple systems and markets.
Comfortable working in cross-functional squads and translating business needs into scalable data capabilities.
Skilled in test-driven development (TDD) and resolving complex technical problems to ensure high-quality data products.