Data Architect/Modeler

Key Responsibilities

• Design and implement enterprise-wide data architecture solutions for large-scale financial services environments.
• Develop conceptual, logical, and physical data models supporting operational, analytical, and reporting platforms.
• Arcitect and support cloud-native data platforms including modern data lake and data warehouse ecosystems.
• Perform hands-on data engineering activities including development of ETL/ELT pipelines, data ingestion frameworks, and transformation processes.
• Design scalable batch and real-time data integration solutions for structured and semi-structured data.
• Support Master Data Management (MDM) initiatives across security, account, client, and reference data domains.
• Collaborate with enterprise architecture, governance, security, compliance, and business teams to establish data standards and best practices.
• Implement data quality, metadata management, lineage, and governance frameworks.
• Optimize data platforms for scalability, reliability, performance, and cost efficiency.
• Support regulatory, audit, risk, and compliance reporting requirements within U.S. financial industry environments.
• Participate in cloud migration and modernization initiatives involving legacy and distributed data systems.
• Enable analytics, reporting, AI/ML, and business intelligence capabilities through trusted and governed enterprise data solutions.

Required Skills & Experience

• 8-10 years of experience in Enterprise Data Architecture and Data Modeling across modern data platforms.
• Hands-on experience with Data Engineering and development of scalable, modern data pipelines.
• Proven experience with cloud-based data platforms and distributed data processing technologies.
• Strong understanding of data warehouses, data lake, and lakehouse architecture, including implementation on Modern data platforms.
• Experience designing and implementing ETL/ELT frameworks using tools native to both platforms (e.g., Spark-based pipelines, Snowflake tasks and streams).
• Experience with data integration and ingestion patterns for large-scale structured and unstructured data across platforms.
• Experience designing modern data platforms for legacy transformation initiatives.
• Experience with Master Data Management (MDM) and enterprise data governance frameworks.
• Knowledge of metadata management, data lineage, data cataloging, and data quality processes.
• Experience working with financial services data domains and regulatory/compliance-driven data environments.
• Strong SQL expertise along with programming/scripting experience in Python, PySpark, or Snowpark.
• Experience with dbt (Data Build Tool) for:
  • Data transformation and modeling
  • ELT pipeline development within Snowflake/Databricks
  • Modular, reusable SQL-based data workflows
  • Data testing, documentation, and version control integration
• Experience with cloud platforms such as Azure, AWS, or GCP, including their integration with Snowflake and Databricks.
• Familiarity with API integration, real-time/streaming data pipelines (e.g., Kafka, Spark Streaming), and event-driven architectures.
• Understanding of security, compliance, and governance standards, including role-based access, data masking, and encryption in both Snowflake and Databricks.
• Experience working in Agile delivery models and collaborating with cross-functional teams.

Preferred Qualifications

• Experience supporting enterprise modernization and cloud transformation initiatives.
• Exposure to real-time analytics and large-scale distributed data platforms.
• Knowledge of data governance and enterprise architecture frameworks.
• Strong communication and stakeholder management skills.
• Financial Services or Investment/ Wealth Management domain experience preferred.

Education

• Bachelor's degree in Computer Science, Information Systems, Engineering, or related field.