Data Engineer - Senior Associate

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 Data Engineer - Senior Associate you will shape data modernization efforts within our Cloud Engineering, Data & Analytics practice, supporting the design and delivery of data engineering solutions that help clients manage, transform, and activate data for business use. As a Senior Associate you will analyze complex problems, manage assigned workstreams, guide junior team members, and build meaningful client connections while navigating ambiguity and maintaining professional and technical standards. In this role at PwC Acceleration Center Bangalore, you will contribute to platform engineering and architecture efforts, develop a deeper understanding of business context, and apply critical thinking to translate data requirements into workable technical solutions.
Responsibilities
Developing and maintaining data pipelines across cloud and analytics platforms to support modernization initiatives
Designing data integration, transformation, and validation processes for reliable reporting and downstream use
Building scalable data models and warehouse structures that support complex business and technical requirements
Automating orchestration workflows using Apache Airflow, Azure Data Factory, or similar tooling
Optimizing query performance, storage patterns, and database operations across Snowflake, Databricks, and other platforms
Collaborating with client teams to translate data requirements into technical designs and implementation plans
Troubleshooting data quality, pipeline, and platform issues while identifying root causes and corrective actions
Reviewing deliverables from junior team members and providing guidance on technical approach and work product
Applying data architecture, security, and anonymization practices within enterprise data environments
Contributing to modernization efforts by evaluating emerging tools, methods, and platform capabilities
What You Must Have
At least a Bachelor's degree
At least 4 years of experience
Oral and written proficiency in English required
Python Proficiency: Minimum 3 years of hands-on experience building applications with Python.
Scalable System Design: Solid understanding of designing and architecting scalable Python applications, particularly for Gen AI use cases, with a strong understanding of various components and systems architecture patterns to make cohesive and decoupled, scalable applications.
Web Frameworks: Familiarity with Python web frameworks (Flask, FastAPI) for building web applications around AI models.
Modular Design & Security: Demonstrated ability to design applications with modularity, reusability, and security best practices in mind (session management, vulnerability prevention, etc.,).
Cloud-Native Development: Familiarity with cloud-native development patterns and tools (e.g., REST APIs, microservices, serverless functions).
Cloud Deployments: Experience deploying and managing containerized applications on Azure/AWS (Azure Kubernetes Service, Azure Container Instances, or similar).
Version Control (Git): Strong proficiency in Git for effective code collaboration and management.
CI/CD: Knowledge of continuous integration and deployment (CI/CD) practices on cloud platforms.
3-5 years of relevant technical/technology experience, with a focus on GenAI projects.
Strong programming skills in Python.
Experience with data processing frameworks like Apache Spark or similar.
Proficiency in SQL and database management systems.
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 coursework or internship exposure in data engineering
Utilizing Databricks, Snowflake, or Azure Data Factory
Applying Apache Airflow, data pipelines, or data transformation concepts
Interpreting data quality, data modeling, and query optimization needs
Navigating client-facing workstreams with changing technical requirements

Work arrangement
No