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Tier-1 brand, mid-level generalist data engineer title, broad required stack and metro hiring drive high competition.
Core data engineering skills are broadly transferable across industries, so background sensitivity is low.
Explicit 5+ years minimum plus mandatory Spark/Hadoop/ETL/cloud/airflow skills enforces strict shortlisting.
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Design and deliver scalable, high-quality data pipelines and modern data warehouse solutions utilizing technologies like Spark, Python, and Hive to support global analytics and decisioning.
Provide technical leadership and mentorship to data engineering teams, establishing best practices for coding standards, version control, and automated workflows.
Leverage modern architectural patterns such as Medallion Architecture and advanced data modeling frameworks to ensure efficient, reliable, and future-ready data solutions.
5+ years relevant work experience with a Bachelor's degree or equivalent education/experience combination as specified for advanced degrees.
Strong proficiency in big data technologies including Hadoop ecosystem (HDFS, Hive, Spark, EMR) and building scalable ETL/data pipelines using orchestration tools (e.g., Apache Airflow, Oozie).
Programming expertise in Python and SQL for data processing, and experience with data visualization tools like Tableau or Power BI.
Experience with version control (git), CI/CD pipelines, cloud data platforms (AWS, Azure), and familiarity with GenAI applied to data engineering workflows.
Experienced in architecting and implementing large-scale, multi-dimensional data solutions for complex, global environments with emphasis on scalability and performance.
Demonstrates leadership in driving technical excellence, mentoring engineers, and collaborating cross-functionally with business and technology stakeholders to deliver impactful data products.
Proficient in adopting modern data architectures and tools, including advanced data governance, quality frameworks, and automation techniques using emerging technologies such as Generative AI.