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Mid-level Data Engineer at a known global firm in metro city increases applicant competition.
Data engineering skills transfer across industries but platform governance and specific toolset add moderate specialization.
Explicit years plus mandatory Python, PySpark, Snowflake, and ETL skills increase filtering stringency.
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Design, develop, test, and deploy production-ready data pipelines and applications using Python, PySpark, and related technologies within a platform-enabled environment.
Build reusable, scalable data solutions that align with shared platform standards, enabling cross-team use instead of one-off pipelines.
Collaborate with cross-functional teams to translate business requirements into robust technical designs while adhering to data governance and platform delivery patterns.
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, ETL development, and strong knowledge of data modeling concepts (normalized and dimensional models).
Working knowledge of AWS, Airflow, Snowflake, Iceberg, SQL, Linux, Docker, GitHub, Terraform; experience with Agile and software development best practices.
Experienced in building and maintaining scalable data platforms with reusable patterns across multiple markets or systems.
Able to work within cross-functional Agile squads and collaborate closely with product, engineering, and analysis teams.
Comfortable with complex production issue debugging, test-driven development, and evolving data governance frameworks.