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Tier-1 brand, mid-level generalist data role in metro with common tech stack increases competition.
Strong data engineering skill requirements are transferable across industries but domain-specific, so medium sensitivity.
Explicit 5+ years requirement plus mandatory Hadoop/Spark/Python/cloud/ETL skills makes filters strict.
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Design and implement scalable, high-performance data pipelines and modern data warehouse solutions using Hadoop ecosystem, Apache Spark, Python, and related big data technologies.
Lead and mentor data engineering teams, establish and enforce best practices for maintainability, scalability, and data quality across global data projects.
Collaborate with global business and technical teams to gather requirements and deliver data solutions that support intelligent decision-making and analytics at scale.
Bachelor's degree with at least 5 years of relevant work experience, OR Advanced degree with at least 2 years of relevant work experience, OR PhD with 0 years of experience.
Proficiency with Hadoop ecosystem technologies (HDFS, Hive, Spark, EMR), Python, SQL, and ETL orchestration tools like Apache Airflow or Oozie.
Experience with data modeling, data quality frameworks, version control (Git), and cloud data platforms (AWS, Azure).
Work Experience Required: 5+ years with Bachelor's degree or equivalent as described above.
Experienced in leading large scale data engineering projects and global data delivery with focus on data quality, scalability, and performance optimization.
Skilled in building modular, reusable data pipeline components and deploying production-grade systems with CI/CD practices.
Familiar with applying modern data architectural patterns like Medallion Architecture and integrating emerging technologies such as Generative AI within data engineering workflows.