IN_Senior Associate_Cloud Data Engineer_Data and Analytics_Advisory_Pan India

PwC South Africa; Bengaluru; On-site; Confidential; Full time

Job summary

PwC in Bengaluru is seeking an experienced Data Engineer to design, build, and optimize scalable data pipelines across AWS, Azure, and GCP. You will work with PySpark, Spark, and SQL to ingest, transform, and warehouse data for analytics.

The role requires 4–7 years in data engineering with cloud experience and a BE/BTech or equivalent qualification; certification in AWS/Azure/GCP is a plus. You should be collaborative, proactive, and able to work independently.

Qualifications

• 4–7 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.
• In‑depth knowledge and hands‑on experience with cloud services (AWS, Azure, GCP).
• Demonstrated ability to optimize performance of Spark jobs.
• Strong problem‑solving skills and 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.
• Python, PySpark, SQL (for AWS/Azure/GCP) mandatory.

Responsibilities

• Design, build, and maintain scalable data pipelines for multiple cloud platforms including AWS, Azure, Databricks, and GCP.
• Implement data ingestion and transformation processes to support efficient data warehousing.
• Utilize cloud services to enhance data processing capabilities (AWS Glue/Lambda, Data Factory, Dataflow, BigQuery, etc.).
• 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 cross‑functional teams to deliver robust data solutions.
• Work on Spark Streaming for real‑time data processing as necessary.

Skills

PySpark

Spark

Python

SQL

Data pipelines

Cloud platforms

DWH concept

Education

BE/BTech or equivalent

ME/MTech

MBA

Tools

AWS

Azure

GCP

Databricks

Job description

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.

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, 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, Pub/Sub, 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 cross‑functional teams to deliver robust data solutions.
• Work on Spark Streaming for real‑time data processing as necessary.

Qualifications

• 4–7 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.
• In‑depth knowledge and hands‑on experience with cloud services (AWS, Azure, GCP).
• Demonstrated ability to optimize performance of Spark jobs.
• Strong problem‑solving 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.
• Python, PySpark, SQL (for AWS/Azure/GCP) mandatory.
• Education: BE/BTech, ME/MTech, MBA, MCA or equivalent.