Job
Description
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 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 & Summary Job Title Cloud Data Engineer (AWS/Azure/Databricks/GCP) Experience 3 8 years in Data Engineering 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. 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 crossfunctional teams to deliver robust data solutions. Work on Spark Streaming for realtime data processing as necessary. Qualifications 3 8 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( AWS/Azure/GCP) 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 3 8 years Education qualification BE/BTECH, ME/MTECH, MBA, MCA Education Degrees/Field of Study required Master of Engineering, Bachelor of Engineering, Master of Business Administration, Bachelor of Technology Degrees/Field of Study preferred Required Skills Node.js 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 {+ 28 more} No
