Line of Service
Advisory
Industry/Sector
Not Applicable
Specialism
Data, Analytics & AI
Management Level
Senior Associate
& Summary
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.
In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.
*Why PWC
At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us
At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. "
Responsibilities
Design and implement scalable forecasting and predictive analytics models to solve complex business challenges across multiple domains.
Develop and optimize data models (logical, physical, dimensional, and semantic) to support analytics, ML, and reporting use cases.
Work with large, complex datasets from multiple sources—cleansing, transforming, and preparing them for analytical consumption.
Build, train, and evaluate ML models using statistical and machine learning techniques to enhance accuracy, performance, and interpretability.
Collaborate with data engineers and cloud teams to integrate ML pipelines into AWS or Azure environments using modern ETL and orchestration tools.
Translate business objectives into technical data solutions and actionable insights through strong analytical reasoning and stakeholder communication.
Ensure data quality, lineage, and consistency through standardized data definitions, metadata documentation, and model versioning practices.
Continuously improve models through retraining, drift detection, and performance monitoring usingMLOpsbest practices.
Required Skills and Experience
Proven expertise in machine learning and statistical modeling for forecasting, demand prediction, or time-series analysis.
Strong data modeling skills across dimensional, relational, and semantic structures.
Advanced proficiency in Python for data wrangling, feature engineering, and ML model development (Pandas, NumPy, Scikit-learn,PyTorchTensorFlow preferred).
Intermediate SQL skills with experience writing efficient queries and optimizing database performance.
Strong analytical and problem-solving mindset with the ability to derive insights and communicate outcomes to technical and business stakeholders.
Mandatory Skill Sets:
Domain experience in retail, supply chain, demand forecasting, or CPG analytics.
Strong interest in emerging areas such as Generative AI or AI-driven forecasting automation.
Exposure to cloud ecosystems (AWS, Azure) including data engineering components like Glue, Data Factory, Lambda, Databricks, or Synapse.
Preferred Skill Sets:
Domain experience in retail, supply chain, demand forecasting, or CPG analytics.
Strong interest in emerging areas such as Generative AI or AI-driven forecasting automation.
Exposure to cloud ecosystems (AWS, Azure) including data engineering components like Glue, Data Factory, Lambda, Databricks, or Synapse.
Years of Experience required:
Senior Associate – 4 to 7 years
Education Qualification:
B.E, B.Tech, M.Tech, M.E, MCA
Education (if blank, degree and/or field of study not specified)
Degrees/Field of Study required: Master of Engineering, Bachelor of EngineeringDegrees/Field of Study preferred:
Certifications (if blank, certifications not specified)
Required Skills
Data Science
Optional Skills
Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, Communication, Creativity, Data Anonymization, Data Architecture, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Databricks Unified Data Analytics Platform, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling, Data Pipeline {+ 27 more}
Desired Languages (If blank, desired languages not specified)
Travel Requirements
Not Specified
Available for Work Visa Sponsorship?
No
Government Clearance Required?
No
Job Posting End Date
May 5, 2026
At PwC, we help clients drive their companies to the leading edge. We’re a tech-forward, people-empowered network with more than 370,000 people in 149 countries. Across audit and assurance, tax and legal, deals and consulting we help build, accelerate and sustain momentum. Find out more at www.pwc.com.
PwC: Audit and assurance, consulting and tax services
PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Content on this page has been prepared for general information only and is not intended to be relied upon as accounting, tax or professional advice. Please reach out to your advisors for specific advice.
