Data Architect

Key Responsibilities:

• Design and build the unified data model for the Operational Data Store, balancing domain-specific context with enterprise integration requirements.
• Define the architectural approach for federating domain data products into a cohesive enterprise data layer without creating monolithic dependencies.
• Develop data transformation specifications, mapping rules, and working examples that preserve domain semantics while enabling cross-domain consistency.
• Establish canonical data models that integrate across domain boundaries while respecting source system ownership and business context.
• Perform source system analysis, data profiling, and gap assessments to understand domain data products and their integration requirements
• Write and validate SQL queries, transformation logic, and data quality rules to prove out architectural decisions before handoff.
• Define system of record ownership across domains and maintain accurate data lineage documentation for federated data sources.
• Design integration patterns that allow domains to evolve independently while maintaining enterprise data consistency.
• Collaborate directly with Data Engineers during implementation, troubleshooting data quality issues and refining transformation logic.
• Partner with domain stakeholders and enterprise architects to align domain data products with cross-domain analytics and operational needs.
• Establish federated data governance standards that balance domain autonomy with enterprise consistency requirements.
• Conduct architecture reviews focusing on data integrity, performance optimization, and scalability of the ODS.
• Ensure compliance with data privacy regulations, security standards, and audit requirements in financial services.
• Stay current with industry trends in Data Mesh, Data Fabric, and regulatory changes in the FinTech sector.

Required Qualifications:

• Bachelor's or master's degree in computer science, Information Systems, Data Science, or related field.
• 10+ years of experience in data management with at least 5+ years in data architecture roles.
• Strong understanding of Data Mesh principles (domain ownership, data as product, federated governance) and Data Fabric concepts (unified access, integration layer, cross-domain visibility).
• Proven expertise in logical and physical data modeling using tools such as ERwin, PowerDesigner, or similar.
• Experience designing canonical data models that integrate multiple domain data sources while preserving business context.
• Strong hands-on SQL skills with ability to write complex queries for data profiling, validation, and transformation.
• Experience building Operational Data Stores, data warehouses, or enterprise data hubs with multiple source system integrations.
• Demonstrated ability to perform data profiling, source system analysis, and data quality assessments independently.
• Hands-on experience developing data transformation specifications and mapping documentation that engineers can implement.
• Working knowledge of ETL/ELT patterns and data pipeline architecture.
• Familiarity with event streaming platforms (Kafka, Kinesis) for real time data ingestion scenarios.
• Solid understanding of data governance, metadata management, and data lineage practices.

Preferred Qualifications:

• Demonstrates judgment and flexibility - positively deals with shifting priorities and rapid change of environments.
• Experience in financial services, insurance, or large scale enterprise data platforms.
• Experience implementing Data Mesh architectures or federated data governance models
• Proficiency with Python or similar scripting language for data analysis and validation.
• Familiarity with cloud data services (AWS Glue, Azure Data Factory, Snowflake, Databricks).
• Knowledge of data catalog and lineage tools (Collibra, Alation, Apache Atlas).
• Exposure to domain-driven design principles applied to data architecture.
• Understanding of regulatory requirements (SOX, GDPR, CCPA) as they apply to data management.
• Experience mentoring Data Engineers and establishing team technical standards.