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Strong PwC brand, metro location, and common mid-level data engineer skillset create high competition.
Core data engineering skills are transferable, but consulting/advisory context raises moderate bias toward similar experience.
Explicit 5-8 years plus mandatory Spark/PySpark/Python and leadership requirements make shortlisting stringent.
Design, develop, and maintain scalable data pipelines and architectures primarily using Spark, PySpark, and Python.
Lead and manage a team of data engineers, including providing technical guidance and managing project delivery timelines and resources.
Collaborate across functions to translate business needs into data engineering solutions while ensuring data quality, integrity, and security.
5-8 years of professional experience in data engineering or related roles.
Strong hands-on expertise with Spark, PySpark, and Python for data processing and transformation.
Bachelor's degree required (B.Tech preferred); M.Tech/MCA/MBA also acceptable.
Experience with cloud-native data engineering platforms such as Databricks, Azure Data Engineering, or AWS is highly desirable.
Experienced leader capable of managing and mentoring a team of data engineers within complex project environments.
Strong technical background in data architecture, ETL processes, and scalable pipeline design in a cloud environment.
Skilled at interfacing with stakeholders to align technical data solutions with business objectives and priorities.