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Mid-tier brand plus broad, common data-engineering skillset increases candidate competition moderately.
Core data engineering skills like ETL, Spark, and cloud are highly transferable across industries.
Explicit 8–12 years requirement, mandatory data engineering and cloud stack experience, and tooling expertise raise filter strictness.
Design, develop, and maintain scalable data pipelines, ETL/ELT processes, and data integration frameworks across structured and unstructured data.
Build and manage enterprise data warehouses, data lakes, and lakehouse architectures using cloud-native services (preferably Azure).
Provide technical leadership including architecture reviews, CI/CD pipeline development, and mentoring junior engineers to support analytics, AI/ML, and business intelligence initiatives.
8–12 years of overall IT experience with at least 5+ years in Data Engineering.
Strong hands-on skills in SQL and Python; experience with cloud-based data platforms, preferably Microsoft Azure.
Proven expertise in building ETL/ELT frameworks, data warehouses, and handling large-scale datasets.
Work Experience Required: 8–12 years overall IT; 5+ years in Data Engineering.
Experienced in cloud platforms, especially Azure, with certifications like Azure Data Engineer Associate or Databricks Certified Data Engineer being an advantage.
Demonstrates technical leadership through architecture design, CI/CD automation, and team mentoring in Agile environments.
Practical expertise in big data technologies (Apache Spark, Kafka), data governance, and building solutions for advanced analytics and AI/ML.