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Financial institutions are looking to build smarter and more efficient ways to operate their business, through new opportunities uncovered by their data. EY's clients look to them as trusted advisors to provide insights on how to ingest, interpret, and manage their data.

Requirements

• Ability to understand business challenges and address remediations through data architecture and engineering solutions.
• Must have practical experience assisting clients with development of one or more of strategic Data Architecture and Engineering, Cloud Data Modernization, Data migration and Event-driven architecture, preferably focused on financial services.
• Experience development of large data platforms, data warehouses and data lakes, streaming applications, data ingestion, integration, and distribution pipelines.
• Practical experience using latest technologies for data ingestion, integration, transformation, storage, mining/warehousing, big data analytics, and visualization.
• Understanding of traditional data architecture practices (data warehousing, datahub, MDM, ODS etc.) as well transition to Next-Gen platform architectures such as distributed data lake and Data mesh on cloud, etc.
• Practitioner experience in multiple data technologies such as: Cloud Data Platforms (AWS, Azure, Google Platform, Databricks, Snowflake)
• Experience with scripting languages: Python, Java, C++, Scala, etc.

Responsibilities

• Ability to understand business challenges and address remediations through data architecture and engineering solutions.
• Must have practical experience assisting clients with development of one or more of strategic Data Architecture and Engineering, Cloud Data Modernization, Data migration and Event-driven architecture, preferably focused on financial services.
• Experience development of large data platforms, data warehouses and data lakes, streaming applications, data ingestion, integration, and distribution pipelines.
• Practical experience using latest technologies for data ingestion, integration, transformation, storage, mining/warehousing, big data analytics, and visualization.
• Understanding of traditional data architecture practices (data warehousing, datahub, MDM, ODS etc.) as well transition to Next-Gen platform architectures such as distributed data lake and Data mesh on cloud, etc.
• Practitioner experience in multiple data technologies such as: Cloud Data Platforms (AWS, Azure, Google Platform, Databricks, Snowflake)
• Experience with scripting languages: Python, Java, C++, Scala, etc.

Other

• Fostering an innovative and inclusive team-oriented work environment
• Working in diverse teams of professionals with different backgrounds
• Demonstrating in-depth technical capabilities and professional knowledge
• Contributing to thought leadership through white papers, point of views, and proof of concepts
• Working in an entrepreneurial environment to pave your own career path