AI Led Data Engineering (AWS Redshift + Kinesis+PySpark) - Senior Associate

Overview

Senior Associate in PwC's Acceleration Center India, Data, Analytics & AI specialism, building data pipelines, integration and transformation solutions that turn raw data into actionable insights for client needs.

What you'll do

• Design and build data pipelines that support cloud-based data processing and analytics
• Develop and maintain AWS Redshift, Kinesis, and PySpark solutions for ingestion, transformation, and delivery
• Translate business requirements into data engineering deliverables that support client analytics and reporting needs
• Apply data modeling, integration, and validation methods to improve reliability and usability of data assets
• Optimize pipeline performance, query execution, and storage patterns across modern data platforms
• Support data modernization efforts by modernizing legacy data flows into scalable cloud architectures
• Review code, logic, and technical output to maintain consistency with engineering standards and project goals
• Collaborate with client teams to interpret data challenges and adjust solutions as business needs change
• Mentor junior team members on engineering tasks, technical approaches, and problem-solving methods

What you'll need

• At least a Bachelor's degree
• At least 5+ years of experience
• Oral and written proficiency in English

Nice to have

• Preference for at least one of the following fields of study: Management Information Systems, Computer and Information Science, Systems Engineering, Electrical Engineering, Chemical Engineering, Industrial Engineering, Mathematics, Statistics, Mathematical Statistics
• Demonstrating AWS Redshift, Kinesis, and PySpark data engineering delivery
• Developing cloud data pipelines, transformations, and warehouse modeling
• Interpreting data to guide client recommendations and project decisions
• Collaborating across teams while managing ambiguity and changing priorities
• Mentoring junior team members through complex technical tasks