Pwc Hiring Drive 2024 For Data Engineer – Gen AI – Associate 2 | Location Hyderabad

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Pwc Hiring Drive 2024: Pwc Hiring Drive 2024 for its Data Engineer – Gen AI – Associate 2. PwC Drive organizes off-campus drives for candidates in any batch. To apply, candidates should have a Bachelor s degree in Computer Science, Data Science, or a related field. if you are interested, please apply as soon as possible.

Company Name: Pwc

Role: Data Engineer – Gen AI – Associate 2

Location: Hyderabad

Experience: 1 – 3 years

Qualification: Bachelor s degree in Computer Science, Data Science, or a related field.

Batch: Any

Job Type: Full Time

Salary: Best in Industry

Pwc Hiring Drive 2024

Job Description:

  • As an Associate, youll work as part of a team of problem solvers, helping to solve complex business issues from strategy to execution.
  • PwC Professional skills and responsibilities for this management level include but are not limited to:
  • Invite and give in the moment feedback in a constructive manner.
  • Share and collaborate effectively with others.
  • Identify and make suggestions for improvements when problems and/or opportunities arise.
  • Handle, manipulate and analyse data and information responsibly.
  • Follow risk management and compliance procedures.
  • Keep up-to-date with developments in area of specialism.
  • Communicate confidently in a clear, concise and articulate manner – verbally and in the materials I produce.
  • Build and maintain an internal and external network.
  • Seek opportunities to learn about how PwC works as a global network of firms.
  • Uphold the firms code of ethics and business conduct.


  • Bachelor s degree in Computer Science, Data Science, or a related field.
  • 1-3 years of relevant experience, ideally with some exposure to GenAI projects.
  • Basic programming skills in Python.
  • Familiarity with data processing frameworks like Apache Spark or similar.
  • Understanding of SQL and basic database management systems.
  • Some knowledge of event-driven architectures and real-time data processing.
  • Exposure to containerization technologies like Kubernetes and Docker.
  • Awareness of data lakes and basic data lake management principles.
  • Some familiarity with LLM frameworks such as Langchain and Semantic Kernel.
  • Experience with cloud computing platforms such as Azure or AWS is a plus.
  • Strong analytical and problem-solving skills.
  • Good communication and collaboration abilities.
  • Ability to work in a fast-paced and dynamic environment.
  • Exposure to additional technologies such as Databricks, Azure AI Search, Azure OpenAI, Azure Event Hub, Azure Data Lake Storage, AWS Open Search, AWS Bedrock, AWS Event Bridge, AWS S3, Azure Key Vault,DataDog, and Splunk.


  • Assist in the design, development, and maintenance of data pipelines and ETL processes for GenAI projects.
  • Work closely with data scientists and software engineers to support the implementation of machine learning models and algorithms.
  • Help optimize data infrastructure and storage solutions to ensure efficient data processing.
  • Gain experience with event-driven architectures to enable real-time data processing and analysis.
  • Learn and assist with containerization technologies like Kubernetes and Docker for deployment and scalability.
  • Support the development and maintenance of data lakes for managing large volumes of structured and unstructured data.
  • Contribute to the integration of LLM frameworks (such as Langchain and Semantic Kernel) for advanced language processing and analysis.
  • Collaborate with cross-functional teams to support the design and implementation of solution architectures for GenAI projects.
  • Utilize cloud computing platforms such as Azure or AWS under supervision for data processing, storage, and deployment.
  • Participate in monitoring and troubleshooting of data pipelines and systems to ensure smooth operations.
  • Stay informed about the latest advancements in GenAI technologies and assist in recommending innovative solutions to enhance data engineering processes.
  • Engage with cross-functional teams to help understand business requirements and contribute to translating them into technical solutions.
  • Assist in documenting data engineering processes, methodologies, and best practices.

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