Overview In this role, you will lead data architecture and engineering work for EY's Financial Services Organization, delivering scalable data platforms and analytics capabilities for clients. You'll work with cross-border teams to tackle complex data challenges and advance data-driven insights in banking, capital markets, insurance, and asset management. You'll build relationships, drive growth, and contribute to thought leadership while navigating an entrepreneurial, client-facing environment. This is a chance to shape how leading financial institutions ingest, govern, and leverage data at scale.
Compensation / Benefits- flexible, hybrid work model
- diverse and inclusive culture
- competitive compensation and benefits package
- medical and dental coverage
- pension and 401(k) plans
- paid time off and holidays
Responsibilities- Deliver data and analytics services as part of diverse, global teams
- Solve complex data issues and drive business growth across financial services
- Lead and coach teams with varied backgrounds
- Provide subject-matter expertise to define technical and business approaches
- Build client relationships and cultivate new business opportunities
- Initiate and publish thought leadership through white papers, POVs, and proofs of concept
- Pave a career path in an entrepreneurial environment
Key requirements- Undergraduate or master's degree in a quantitative field
- 6+ years of relevant experience in banking, capital markets, insurance, or asset management
- Strong verbal and written communication to convey technical solutions to diverse audiences
- Proven ability to work independently under tight deadlines
- Strong analytical and problem-solving skills
- Experience with client-facing activities: requirements gathering, meetings, deliverables
- Prior project management and client-servicing experience
- Excellent leadership and teamwork skills
- Strong organizational and time-management abilities
- Willingness to travel up to 60% and valid passport
- DAE-specific: ability to address business challenges with data architecture and engineering solutions
- Experience with strategic Data Architecture and Engineering, Cloud Data Modernization, Big Data initiatives, and Event-driven architecture in financial services
- Experience architecting large data platforms, pipelines, data warehousing, and ingestion/integration
- Hands-on use of modern data technologies for ingestion, transformation, storage, analytics, and big data
- Solid understanding of traditional data architectures and transition to Next-Gen platforms
- Technologies: Cloud Data Platforms (AWS, Azure, Google Platform, Databricks, Snowflake); Hadoop ecosystem; RDBMS (MS SQL Server, Oracle, MySQL, PostgreSQL); MPP (Redshift, Teradata, Netezza); NoSQL (MongoDB, DynamoDB, Cassandra, Neo4J, Elasticsearch); Streaming (Spark, Kafka, Storm); DevOps (GitHub, Kubernetes, Jenkins, Terraform); Data Modeling (Datavault, Star, Snowflake)
- Leadership and team collaboration
- Effective communication with diverse stakeholders
- Curiosity and adaptability
- Cloud Data Platforms (AWS, Azure, Google Platform, Databricks, Snowflake)
- Big Data and distributed processing (Hadoop ecosystem)
- Traditional RDBMS (MS SQL Server, Oracle, MySQL, PostgreSQL)
