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Tier-1 brand, common Data Engineer title, mid-level experience, and broad GCP/Python/BI requirements increases applicant competition.
Core data engineering skills are transferable across industries, though GCP and enterprise compliance slightly increase domain specificity.
Explicit 6–8 year requirement plus mandatory GCP, Python, SQL, and enterprise compliance needs tighten shortlisting.
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Collaborate with stakeholders to design scalable, secure data architecture and access layers for complex data solutions.
Engineer high-performance automated ETL/ELT pipelines and advanced data visualization for business intelligence.
Own end-to-end data orchestration, including incident automation, system health monitoring, performance tuning, and compliance enforcement.
6-8 years of experience building and maintaining enterprise-level data applications.
Bachelor's degree or equivalent practical experience.
Proficiency in Python, SQL, Google Cloud Platform (GCP), and business intelligence tools (Lookerstudio, Data Studio, Tableau, or PowerBI).
Exceptional detail orientation and ability to follow detailed workflows independently.
Experienced in designing end-to-end data infrastructure with a strategic, scalable, and operational mindset.
Skilled in advanced data pipeline engineering and automation for reliability and performance.
Comfortable working independently to diagnose and resolve complex data system issues and managing stakeholder interactions.