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Tier-1 brand, mid-level generalist role, metro location, and broad technical requirements increase competition.
Data engineering and cloud skills are transferable, but platform and governance experience increases domain specificity.
Explicit 6–8 years, leadership requirement, and specific data stack mandate make shortlisting strict.
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Manage the full data engineering development lifecycle to ensure timely and business-aligned project delivery.
Oversee risk and quality control by tracking project metrics, resolving issues, and maintaining high team productivity.
Collaborate directly with analytics and business leaders to align technical plans with business objectives.
6 to 8 years of experience in data engineering including prior team management roles.
Strong development skills with AWS or Azure cloud platforms, proficiency in SQL, Python, and PySpark.
Solid knowledge of database design, data modelling, data management, governance, and building semantic data models.
Experience with Agile methodologies, Scrum practices, and project tracking tools like Jira.
Experienced in managing technical delivery and bridging data engineering with data science for business impact.
Comfortable working in a fast-paced environment requiring direct stakeholder interaction and alignment.
Skilled in modern cloud data stacks and governance, capable of overseeing end-to-end data platform development and optimization.