What if your most rewarding experiences couldn’t be captured by a resume?  

At EY we believe that whoever you are, your uniqueness helps us stand apart.   

We bring together extraordinary people, like you, to build a better working world.  

EY DnA is the data and advanced analytics capability within EY Asia-Pacific, with over 500 specialist employees working across multiple industry sectors. 

What’s in it for you?  

As a Senior Data Engineer, you will support our clients in defining and implementing a data journey aligned with their strategic objectives. Some of your responsibilities will include:

• Be able to clearly explain data and analytics strengths and weaknesses to both technical and senior business stakeholders
• Establishing the required data capabilities of the Azure data platform and its use case
• Responsible for developing batch ingestion and data transformation routines using ETL tools or other ingestion techniques
• Responsible for developing real-time ingestion and data transformation routines using Kafka and similar technologies 
• Responsible for migrating data from legacy data platform to cloud data platform
• Present data in graphs, charts, tables, etc. and designing and developing relational databases for collecting data 

What we’re looking for 

Here’s our ‘wish list’ but don’t worry if you don’t tick all the boxes. We’re interested in your strengths, what you want to learn, and how far you want to go.  

• Configuration and coordination of data pipelines across projects
• Data pipeline development, including Azure Data Factory and Spark
• Migration and transformation of large complex data sets from legacy systems like Teradata to cloud platform using Azure tools and other ETL tools like DataStage
• Data and Reporting Platform development on environments using technologies such as Microsoft Azure, SAP BW, Teradata, PowerBI or Cognos
• A strong background in coding with possible experience with Python, SAS, SQL and/or R
• Project delivery toolset experience in one or more batch ETL tools (such as DataStage, Informatica, Microsoft SSIS, Azure Data Factory or Talend) and/or open-source data integration tools (such as Kafka or Nifi)
• Spark and PySpark experience are mandatory to be successful in this role