AWS Glue Data Engineer(Redshift+Pyspark+Kinesis) - Senior Associate

Industry/Sector

Specialism

Management Level

Job Description & Summary

The Opportunity

Join our Acceleration Center India and help shape the future of business for our diverse client portfolio across geographies and jurisdictions. You’ll work at the heart of global teams across Advisory, Assurance, Tax and Business Services—solving real client challenges through connected collaboration. We’ll help you grow your skills so you can go further. With hands-on learning, cutting-edge tools and an inclusive culture, this is your opportunity to do inspiring work that makes a difference—every day.

As a AWS Glue Data Engineer (Redshift+Pyspark+Kinesis) you will design and build data pipelines, integration flows, and transformation solutions that help turn raw data into usable insights within our Data and Analytics Engineering practice. As a Senior Associate you will manage assigned workstreams, use critical thinking to break down complex problems, and support junior team members while building meaningful client connections and growing your technical skills. In this role, you will apply AWS Glue, Redshift, PySpark, and Kinesis to develop data infrastructure, work through data movement and transformation needs, and contribute to delivery across client support engagements.

In this role at PwC, you will focus on data engineering within our Data, Analytics & AI capability and the Data Modernisation go-to-market area, contributing to cloud-based data platforms that support processing, integration, and analytics. You will review data patterns, interpret findings to inform recommendations, and maintain professional and technical standards while adapting to changing client needs. This role offers the chance to deepen your understanding of business context, strengthen your personal brand, and develop experience across multiple data workstreams.

Responsibilities

- Designing and building AWS Glue data pipelines to move, transform, and load data across Redshift and cloud data stores
- Developing PySpark jobs for batch and streaming processing, with attention to code readability, performance, and maintainability
- Integrating Kinesis data streams into ingestion workflows for near real-time processing and downstream analytics use cases
- Creating data models and transformation logic that support warehouse development, reporting, and analytical access patterns
- Tuning query performance and pipeline throughput in Redshift, Spark, and related data engineering platforms
- Troubleshooting data integration issues by reviewing source systems, transformation logic, and downstream dependencies
- Collaborating with client stakeholders to gather requirements, clarify data needs, and translate them into technical deliverables
- Reviewing work from junior team members and providing guidance on data pipeline design, development standards, and delivery approach
- Applying cloud engineering and data modernization methods to support scalable data platform implementation and migration activities

What You Must Have

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

What Sets You Apart

- 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 hands-on AWS Glue and PySpark data pipeline development
- Developing Redshift-based data models and performance tuning approaches
- Utilizing Kinesis streaming to build real-time data ingestion solutions
- Applying data engineering tools across cloud modernization initiatives
- Interpreting complex data needs and translating them into deliverables
- Mentoring junior team members while navigating client workstreams

Travel Requirements

Job Posting End Date