Position Summary:

We are seeking a highly skilled and experienced Data Architect with deep expertise in Salesforce and nCino platforms to lead and execute complex data architecture initiatives, especially in the context of core banking transformations, CRM migration, and data modernization efforts. The ideal candidate will have a proven track record of performing data mapping from legacy systems, building logical and physical data models, and conducting hands-on data analysis to drive enterprise decision-making, data integrity, and platform interoperability.

Key Responsibilities:

Data Architecture & Platform Integration

• Design, develop, and maintain robust end-to-end data architecture solutions for Salesforce and nCino platforms aligned with enterprise strategy.
• Partner with application owners, product teams, and data engineers to architect integration frameworks between Salesforce/nCino and other systems (core banking, data lakes, warehouses).
• Drive the definition and implementation of canonical data models to enable consistent data sharing and consumption across systems.

Legacy System Mapping & Data Migration

• Lead discovery efforts to understand data structures of legacy banking and CRM systems (e.g., FIS, Jack Henry, Hogan, etc.).
• Create detailed data mapping specifications, identifying source-to-target transformation logic to support Salesforce and nCino onboarding.
• Collaborate with ETL teams to validate and refine data mappings for high-quality data migration and synchronization.

Data Modeling & Governance

• Design conceptual, logical, and physical data models for Salesforce/nCino custom and standard objects, integrating them into the broader enterprise data model.
• Define data architecture standards, naming conventions, and best practices for object modeling and metadata management.
• Ensure models adhere to data governance, privacy, and compliance policies (e.g., GLBA, CCPA).

Data Analysis & Validation

• Perform data profiling, quality checks, and statistical analysis to assess data readiness, accuracy, and completeness across systems.
• Support UAT and production readiness by building test cases and validating data migration success for Salesforce and nCino platforms.
• Partner with business analysts and product owners to translate business requirements into actionable data models and structures.

Collaboration & Thought Leadership

• Serve as a technical liaison between business teams, platform engineers, and data stewards to ensure seamless data integration.
• Provide technical leadership and mentorship to junior architects, modelers, and data analysts.
• Stay current with emerging trends in data architecture, Salesforce/nCino development, and banking industry standards.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or related field.
• 8+ years of experience in enterprise data architecture roles, including 3+ years hands-on with Salesforce and nCino implementations.
• Strong expertise in data mapping and migration from legacy core banking and CRM platforms.
• Proficiency in data modeling tools such as Erwin, ER/Studio, or SQL Power Architect.
• In-depth understanding of Salesforce and nCino object models, metadata, and data schema configurations.
• Hands-on experience in data profiling, SQL queries, and using tools like Informatica, Talend, or AWS Glue.
• Familiarity with REST APIs, JSON, and integration patterns for real-time and batch data movement.

Preferred Qualifications:

• Salesforce certifications (e.g., Salesforce Data Architect, Platform Developer, nCino Functional Consultant) are highly desirable.
• Experience working with data lake architectures, cloud-native data platforms (e.g., Snowflake, Redshift, Databricks).
• Knowledge of Agile delivery, DevOps practices, and CI/CD pipelines.
• Experience with metadata management tools such as Collibra or Informatica EDC.

Key Competencies:

• Strategic and analytical thinking
• Attention to detail with strong data intuition
• Effective verbal and written communication
• Cross-functional collaboration and influence
• Ability to navigate ambiguity and drive clarity in large data programs