Offer summary

Qualifications:

Bachelor's or Master's degree in Engineering, Technology, or related fields., 3-8 years of experience in data engineering, with cloud platform expertise., Proficiency in Python, PySpark, and SQL., Knowledge of cloud services like AWS, Azure, or GCP and experience with data pipelines..

Key responsibilities:

Design, build, and maintain scalable data pipelines on cloud platforms.

Implement data ingestion, transformation, and warehousing processes.

Optimize Spark job performance and collaborate with cross-functional teams.

Work on real-time data processing using Spark Streaming.

PwC Acceleration Centers (ACs), formerly Service Delivery Centers, are diverse, global talent hubs focused on delivering value to our clients – working with global client engagement teams across the PwC network. Driving new digital ways of working, ACs help unlock new opportunities for our people, organization and clients through competency enhancement, collaboration, automation, quality, innovation and integrated global delivery.Our Acceleration Centers span 11 cities and seven countries, and provide virtual client engagement support around the world and across all of PwC’s services and capabilities.Professionals in our innovative Acceleration Centers (ACs) are part of inclusive teams who work together across functions and geographies; engage in challenging projects; and provide distinctive services to support client engagements through enhanced quality and innovation. A role in our Acceleration Centers will provide growth potential and valuable learning opportunities.

Line of Service

Advisory

IndustrySector

Not Applicable

Specialism

Data, Analytics & AI

Management Level

Senior Associate

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 decisionmaking 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 purposeled and valuesdriven 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:

Job Title: Cloud Data Engineer (AWSAzureDatabricksGCP) Experience:38 years in Data Engineering Job Description: We are seeking skilled and dynamic Cloud Data Engineers specializing in AWS, Azure, Databricks, and GCP. The ideal candidate will have a strong background in data engineering, with a focus on data ingestion, transformation, and warehousing. They should also possess excellent knowledge of PySpark or Spark, and a proven ability to optimize performance in Spark job executions. Key Responsibilities: Design, build, and maintain scalable data pipelines for a variety of cloud platforms including AWS, Azure, Databricks, and GCP. Implement data ingestion and transformation processes to facilitate efficient data warehousing. Utilize cloud services to enhance data processing capabilities: AWS: Glue, Athena, Lambda, Redshift, Step Functions, DynamoDB, SNS. Azure: Data Factory, Synapse Analytics, Functions, Cosmos DB, Event Grid, Logic Apps, Service Bus. GCP: Dataflow, BigQuery, DataProc, Cloud Functions, Bigtable, PubSub, Data Fusion. Optimize Spark job performance to ensure high efficiency and reliability.

Stay proactive in learning and implementing new technologies to improve data processing frameworks. Collaborate with crossfunctional teams to deliver robust data solutions. Work on Spark Streaming for realtime data processing as necessary. Qualifications: 38 years of experience in data engineering with a strong focus on cloud environments. Proficiency in PySpark or Spark is mandatory. Proven experience with data ingestion, transformation, and data warehousing. Indepth knowledge and handson experience with cloud services(AWSAzureGCP): Demonstrated ability in performance optimization of Spark jobs. Strong problemsolving skills and the ability to work independently as well as in a team. Cloud Certification (AWS, Azure, or GCP) is a plus. Familiarity with Spark Streaming is a bonus.

Mandatory skill sets:

Python, Pyspark, SQL with (AWS or Azure or GCP)

Preferred skill sets:

Python, Pyspark, SQL with (AWS or Azure or GCP)

Years of experience required:

38 years

Education qualification:

BEBTECH, MEMTECH, MBA, MCA