Description

You will design and implement enterprise-wide data architecture solutions for large-scale financial services environments, owning the end-to-end lifecycle from conceptual modeling to cloud-native deployment.

This role is hybrid.

Responsibilities

• Develop conceptual, logical, and physical data models for operational, analytical, and reporting platforms.
• Architect cloud-native data lakes, warehouses, and lakehouse ecosystems aligned with security and governance standards.
• Build and optimize scalable data pipelines using Spark, PySpark, Snowflake, and Databricks for batch and real-time integration.
• Implement Master Data Management (MDM) platforms and Golden Source solutions for security, account, and client data domains.
• Migrate legacy on-premises platforms (e.g., Oracle Exadata) to cloud environments while ensuring data quality and lineage.

Required Skills

• 8–10 years of experience in Enterprise Data Architecture and Data Modeling.
• Strong hands-on experience with Databricks, Snowflake, and modern cloud data platforms.
• Proven expertise in developing ETL/ELT frameworks using Spark, Snowflake Tasks, and Streams.
• Strong proficiency in dbt (Data Build Tool) for modular, reusable SQL-based data transformations and testing.
• Experience with MDM platforms and Golden Source data management.
• Proficiency in Python, PySpark, or Snowpark for data engineering tasks.
• Experience migrating Oracle-based or legacy data warehouses to cloud environments.
• Understanding of data governance, metadata management, lineage, and quality frameworks.
• Familiarity with Azure, AWS, or GCP integration with Snowflake and Databricks.

Preferred Skills

• Experience with real-time data processing using Kafka or Spark Streaming.
• Strong background in U.S. financial services regulatory and compliance environments.

Key Skills

Education

Bachelor's degree