Role Overview
Lead the design of a modern, governed data foundation for a community bank moving from siloed systems toward an enterprise architecture that can support AI. You will own the current-state assessment, target-state architecture, warehouse and integration design, and data governance across core banking, wealth, and CRM systems, and Client the data groundwork for downstream use cases such as a relationship-manager copilot, Customer 360, and automated loan decisioning. This is a hands-on, client-facing architect role, not a purely advisory one.
Key Responsibilities
- Assess the current state. Inventory data sources, flows, and gaps across FIS IBS core, InfoBanc/MaUI wealth, Salesforce FSC and Marketing Cloud, and the commercial, consumer, and mortgage LOS.
- Define the target architecture. Design a warehouse or lake approach on SQL, Snowflake, or Databricks, sized to ~80,000 accounts and scalable well beyond, with a clear migration path off Salesforce-as-warehouse.
- Design integration and pipelines. Specify ETL and vector-ETL and MuleSoft integration to de-silo and reconcile fragmented data into clean, governed inputs for analytics and AI.
- Model Customer 360. Design the data model that unifies retail, commercial, and wealth relationships into a single, trusted customer view.
- Stand up governance and quality. Establish data governance, modeling, lineage, and quality standards that extend the client's existing AI governance committee and satisfy examiner expectations for audit logging and documentation.
- Enable downstream AI. Prepare the foundation for RAG and LLM use cases, including fair-lending and adverse-action data considerations for any model touching a credit decision.
- Advise and mentor. Produce architecture briefs, data strategy, and roadmaps for leadership and budget planning, and coach the client's Salesforce-focused team toward permanent in-house data capability.
Required Qualifications
- 8+ years in data architecture / engineering, including enterprise warehouse and lake design.
- Cloud data platforms hands-on Snowflake, Databricks, Azure or AWS, and SQL Server.
- ETL/ELT and integration experience; MuleSoft or a comparable iPaaS.
- Data modeling (dimensional and/or data vault), governance, lineage, and quality frameworks.
| - Financial services experience, ideally banking across core, wealth, and lending, with regulatory and data-privacy context.
- Salesforce data model familiarity; Financial Services Cloud a strong plus.
- AI/ML data preparation experience readying data for ML and LLM/RAG use cases, including vector stores.
- Client-facing communication able to translate architecture into decisions executives can fund.
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Preferred
FIS IBS or comparable core exposure and wealth-platform familiarity Snowflake, Databricks, or Azure/AWS data certifications prior consulting or advisory delivery experience de-siloing a CRM-as-warehouse into a purpose-built data platform.
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