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Tier-1 brand, metro location, mid-level generalist data role, and common experience band increase applicant competition.
Role requires cloud and big-data platform skills, so candidates from other industries are moderately transferable.
Explicit 4-8 years plus many mandatory Azure, Databricks, Hadoop, Spark and SQL requirements create high shortlisting rigidity.
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Lead end-to-end implementation of Azure cloud data engineering solutions using services like Azure Databricks, Data Factory, Synapse, and Storage (Blob, ADLS, PaaS SQL).
Design, develop, and optimize data pipelines and workflows to enable efficient data processing and analytics.
Mentor junior data engineers and collaborate with cross-functional teams to deliver analytics projects and maintain data governance and security standards.
4+ years hands-on experience with Azure data engineering technologies (ADLS, Data Bricks, Data Flows, HDInsight, Azure Analysis Services).
Proven expertise with Big Data frameworks including mandatory Hadoop and Spark experience.
Strong SQL skills (joins, groups, functions, stored procedures) and Unix shell scripting.
Bachelor’s degree in Engineering, MCA, M.Sc, M.E, M.Tech, or MBA.
Experienced in planning, organizing, and leading cloud development within agile teams.
Familiar with data management concepts, data modeling, and data migration lifecycles relevant to large-scale cloud data projects.
Certified or highly proficient in Azure and Databricks technologies, with additional skills in DevOps tools like Jenkins or GitLab and knowledge of Python preferred.