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Tier-1 brand, metro location, and broad data engineering skillset increase candidate competition.
Medium because technical data engineering skills transfer, but leadership and consultancy experience add domain specificity.
High due to explicit 12+ years, leadership requirement, and detailed mandatory technical skillset.
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Lead and mentor a technical team of 8–15 engineers to design and deliver data engineering solutions across cloud platforms (Azure, AWS, GCP).
Architect, develop, and optimize scalable ETL/ELT pipelines and data architectures, including data lakes, lakehouses, and data warehouses using technologies like Python, PySpark, and Databricks.
Define standards for data operations, governance, monitoring, CI/CD, and collaborate with business and analytics teams to ensure end-to-end project delivery and cloud platform modernization.
12+ years of experience in data engineering with at least 3 years leading large technical teams.
Strong hands-on expertise with Python, PySpark, Databricks (including Lakehouse & Databricks Workflows).
Experience designing and deploying data solutions on Azure, AWS, or GCP and proficiency in related big data platforms and warehouses (e.g., Hadoop, Teradata, Snowflake).
Work Experience Required: 12+ years in data engineering; Notice Period: Not explicitly mentioned in the JD.
Experienced technical leader skilled in managing mid-sized teams delivering cloud data engineering projects using modern data architectures and pipelines.
Strong expertise in multiple cloud platforms and big data technologies with a focus on scalable, maintainable code and DevOps automation.
Capable of bridging technical solutions and business needs, communicating complex concepts effectively to senior management and diverse stakeholders.