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PwC brand, metro location, mid-level generic data engineering role with common cloud and Spark skills increases applicant competition.
Core data engineering skills (Spark, PySpark, Python, cloud) are highly transferable across industries.
Mandatory 5-8 years plus specific Spark, PySpark and Python requirements make shortlisting stringent.
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Lead design, development, and maintenance of scalable data pipelines and architectures using Spark, PySpark, and Python.
Manage and mentor a team of data engineers, ensuring data quality, integrity, and security across projects.
Collaborate with cross-functional teams and stakeholders to translate business needs into technical data engineering solutions and manage project delivery timelines and resources.
5-8 years of professional experience in data engineering or related roles.
Strong hands-on expertise with Spark, PySpark, and Python in data engineering contexts.
Bachelor’s degree in Engineering (B.Tech) or equivalent (M.Tech/MCA/MBA also accepted).
Experience or knowledge of cloud-native data engineering platforms such as Databricks, Azure Data Engineering, or AWS is highly desirable.
Experienced in leading data engineering projects with proven track record of building scalable data pipelines and architectures.
Demonstrates strong leadership and team management skills suitable for mentoring and guiding engineers.
Comfortable working across cloud platforms (Databricks, Azure, AWS) and evolving data engineering technologies.