Databricks+Azure AI - Associate
Pwc (Crm Experienced Careers Site) Open At a glance
Junior Data Engineering role at Pwc (Crm Experienced Careers Site). Bangalore · full-time.
Pay not stated Data Engineering salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and reshape Databricks pipelines with Azure AI tools for client data modernization work
- Bring data from different sources into cloud platforms for analysis and reporting
- Create transformation logic and reusable components, then check data quality across processing stages
- Model data for analytics and troubleshoot pipeline, query, or platform issues
- Use Python, SQL, and cloud services to automate tasks while documenting requirements and delivery progress
What you bring
- You have at least a bachelor's degree and two years of experience
- You can communicate in English in both writing and conversation
Who this fits
This associate role suits someone developing data engineering skills and ready to learn across changing client assignments. You’ll contribute alongside senior teammates on project delivery.
From the employer
Industry/Sector
Not ApplicableSpecialism
Data, Analytics & AIManagement Level
AssociateJob 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 Databricks Azure AI Associate you will support data engineering work that designs and builds data pipelines, data integration, and data transformation solutions within our Data and Analytics Engineering practice. As an Associate you will focus on learning, adapting to varied client needs, contributing to project tasks, and developing the skills needed to deliver quality work in a fast-paced environment. In this role at PwC, you will help turn raw data into insights that support informed decisions, strengthen data infrastructure, and build your technical capability while working with cloud data technologies and AI-enabled solutions.
Responsibilities
- Building and transforming data pipelines using Databricks and Azure AI tools to support client data modernization work
- Integrating data from multiple sources into cloud-based data platforms for analysis and reporting
- Developing data transformation logic and reusable components that support scalable engineering solutions
- Validating data quality, completeness, and consistency across ingestion, processing, and storage layers
- Supporting data modeling activities that organize information for analytics and downstream use
- Troubleshooting pipeline failures, query issues, and platform errors to keep delivery moving
- Applying Python, SQL, and cloud data services to automate routine engineering tasks
- Collaborating with senior team members to gather requirements, document technical details, and track work through delivery milestones
What You Must Have
- At least a Bachelor's degree
- At least 2 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
- Developing Databricks and Azure AI skills through coursework or projects
- Applying data engineering concepts, including pipelines and data modeling
- Using Python, SQL, or cloud tools in academic or internship settings
- Gathering facts, analyzing patterns, and sharing clear ideas
- Adapting quickly in client-facing, fast-paced team environments
Travel Requirements
Not SpecifiedJob Posting End Date
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