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Mid-level data engineering role with common Python/SQL skills attracts moderate applicant density.
Core data engineering skills (Python, SQL, ETL) transfer well across industries.
Explicit 4–7 years requirement plus mandatory Python/Pandas/SQL skills raises shortlisting strictness.
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Design, build, and maintain optimized ETL/ELT pipelines for data warehousing using Python, Pandas, SQL, and Polars.
Develop high-performance, parallelized data transformations focused on speed, memory efficiency, and reliability.
Perform data validation, monitoring, troubleshooting, and documentation to ensure analytics-ready datasets and pipeline stability.
4–7 years of total experience in Python or data engineering roles.
3–4 years of hands-on Python development experience.
1–2 years of SQL/PLSQL development experience including strong knowledge of window functions and query optimization.
Experience in building optimized data pipelines with parallel processing and production support.
Experienced in scalable data warehousing and ETL/ELT pipeline development with a focus on performance optimization.
Proficient in using Python data libraries like Pandas and preferably Polars for complex data transformations.
Skilled in crafting complex SQL queries with window functions and debugging production data workflows.