IN_Senior Associate_Data Engineering_GCC_Advisory_Mumbai
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Protocol Intelligence
Data-driven signals on your job's competitivenessPwC brand, common mid-level data engineer title, metro location, and broad skillset requirements raise competition.
Core data engineering skills (Spark, PySpark, cloud) are broadly transferable across industries.
Mandatory 5-8 years, required Spark/Databricks certification, and specific tech stack make filters highly strict.
Job Description
Structured overview of role & requirementsAbout This Role
Lead and manage data engineering engagements focused on designing, developing, and maintaining scalable data pipelines using Spark, PySpark, and Python.
Provide technical guidance, mentorship, and performance management to a team of data engineers.
Collaborate cross-functionally to understand data requirements, ensure data quality and security, and manage project delivery including timelines, resources, and budgets.
Minimum Requirements
5-8 years of experience in data engineering or related roles.
Mandatory certifications: Spark 3.0 and/or Databricks Advanced/Professional Architect.
Mandatory skills: expertise in Spark, PySpark, Python, and strong understanding of data warehousing and ETL processes.
Educational qualification: Bachelor’s degree in Engineering/Technology (B.Tech/M.Tech) or MCA/MBA.
Ideal Candidate Profile
Experienced in cloud-native data engineering platforms such as Databricks, Azure Data Engineering, or AWS.
Proven leadership capability to manage and mentor data engineering teams and handle project stakeholder communication.
Strong hands-on technical skills in building scalable data pipelines and architecting data solutions with a focus on best practices and data security.