Sr Associate_ AWS Data Engineer_Data and Analytics_Advisory

Job Summary

At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals.

In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems.

Role overview

We are looking for an experienced AWS Data Engineer with 4-8 years of hands-on experience in designing, developing, and maintaining scalable data pipelines and cloud-based data platforms.

The ideal candidate will have strong expertise in AWS, Snowflake, Apache Airflow, Python, PySpark, and SQL, with a solid understanding of data warehousing, ETL/ELT, data modeling, and performance optimization.

The candidate will work closely with data architects, analysts, application teams, and business stakeholders to build reliable and scalable data solutions.

Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows using AWS services.
• Build and orchestrate data pipelines using Apache Airflow, including DAG development, scheduling, monitoring, retries, dependencies, and error handling.
• Develop data processing and transformation solutions using Python and PySpark.
• Design and implement data warehouse solutions using Snowflake.
• Develop complex SQL queries, stored procedures, views, CTEs, and data transformations.
• Work with AWS services such as S3, Glue, Lambda, EMR, Athena, Redshift, and IAM.
• Build batch and, where required, near-real-time data ingestion pipelines.
• Implement data ingestion from APIs, databases, files, and other source systems into AWS/Snowflake.
• Perform Snowflake performance and cost optimization, including warehouse sizing, query optimization, clustering, partitioning, and efficient data loading.
• Implement Snowflake features such as Snowpipe, Streams, Tasks, stages, file formats, and secure data sharing.
• Develop scalable Spark/PySpark jobs and optimize transformations, joins, partitioning, caching, and resource utilization.
• Implement data quality checks, validation, reconciliation, and monitoring mechanisms.
• Troubleshoot pipeline failures, data issues, performance bottlenecks, and production incidents.
• Follow best practices for data security, governance, access control, and PII-sensitive data handling.
• Use Git and CI/CD practices for source control, automated testing, and deployment of data pipelines.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.