IN_Senior Associate_ Azure Data Engineer_Data and Analytics_Advisory_Pan India

Job Description & 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

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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.

Job Description & Summary

A career within Data and Analytics services will provide you with the opportunity to help organisations uncover enterprise insights and drive business results using smarter data analytics. We focus on a collection of organisational technology capabilities, including business intelligence, data management, and data assurance that help our clients drive innovation, growth, and change within their organisations in order to keep up with the changing nature of customers and technology. We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge.

Responsibilities:

Responsibilities: · Key Responsibilities: ·

• Lead the development and deployment of AIML/generative AI models, managing full project lifecycles from conception to delivery.
• Collaborate with senior stakeholders to identify strategic opportunities for AI applications, aligning them with business objectives.
• Collaborate/Oversee teams of data scientists and engineers, providing guidance, mentorship, and ensuring high-quality deliverables.
• Drive research and innovation in AI techniques and tools, fostering an environment of continuous improvement and learning.
• Ensure compliance with data governance standards and ethical AI practices in all implementations.
• Present AI insights and project progress to clients and internal leadership, adapting technical language to suit audience expertise levels.

Mandatory skill sets:

AI/ML, NLP, and Generative AI models

Preferred skill sets:

Azure, Google Cloud

Years of experience required:

3-5 Years

Education qualification:

Advanced degree (Master’s or Ph.D.) in Computer Science, Artificial Intelligence, Data Science, or related discipline.

Education

Degrees/Field of Study required: Bachelor Degree, Master Degree

Required Skills

Data Engineering

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}