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Moderate brand, common data-engineering skills and senior mid-level profile produce medium competition.
Core data engineering skills are highly transferable across industries.
Multiple explicit years and technical stack requirements enforce high shortlisting strictness.
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Lead design and implementation of automated ELT/ETL data pipelines across diverse data stores and cloud platforms.
Drive complex data engineering initiatives independently with focus on data modeling, integrity, and SQL performance optimization.
Develop and maintain data analytics platforms leveraging Python, SQL, workflow orchestration tools (e.g., Airflow), and cloud technologies (preferably AWS).
8–10+ years of experience in data engineering, data design, and analytics platforms.
5+ years hands-on development experience using Python and SQL.
4+ years experience with cloud platforms (AWS preferred) and workflow orchestration tools such as Airflow.
Proven skills in SQL performance tuning, use of various data storage technologies (relational, columnar, NoSQL, cloud warehouses), and Git-based version control with CI/CD pipelines.
Experienced in independently managing complex data engineering projects with strong data modeling and problem-solving abilities.
Proficient with cloud-native data environments and automation tools, aligning with advanced analytics needs.
Capable of cross-functional communication and multitasking, suitable for leadership within data engineering roles.