AWS Architect Data Engineering (Kinesis+ Redshift + Glue +PySpark) - Senior Manager

Overview

Lead the design and delivery of AWS data engineering solutions using Kinesis, Redshift, Glue, and PySpark, translating business needs into cloud-based data pipelines and analytics-ready platforms within PwC's Advisory practice.

What you'll do

• Lead AWS-based data engineering workstreams across client engagements and translate data modernization goals into actionable solution designs
• Architect data pipelines and data integration patterns using Kinesis, Redshift, Glue, and PySpark for batch and streaming use cases
• Design cloud data platforms that support ingestion, transformation, storage, and downstream analytics across enterprise environments
• Guide technical teams through complex migration and modernization efforts, balancing delivery needs, risks, and dependencies
• Review data architecture choices, performance tuning approaches, and data quality controls to improve reliability and processing efficiency
• Advise clients on scalable lake, warehouse, and transformation solutions that support reporting, analytics, and business insight
• Apply hands-on knowledge of AWS services and data engineering platforms to shape implementation plans and resolve architecture issues
• Coach team members on delivery methods, technical tradeoffs, and documentation standards across multiple workstreams
• Collaborate with stakeholders to validate solution direction, clarify requirements, and align technical outcomes with business priorities
• Drive continuous improvement in data engineering processes, reusable components, and deployment practices

What you'll need

• At least a Bachelor's degree
• At least 13+ 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 data engineering experience using Kinesis, Redshift, and Glue
• Applying PySpark for scalable data transformation and analytics pipelines
• Leading client-facing modernization efforts across cloud data platforms
• Translating complex architecture choices into clear stakeholder messaging
• Coaching teams through ambiguity, prioritizing decisions, and resolving blockers