The Opportunity:The AI Data and Integration Engineer reports to the AI Engineering Lead and is the single accountable owner for making trusted, governed data and reliable system connectivity available to First National's AI enabled mortgage workflows, from design through production.
This is a hands on engineering role within the AI Platform Squad, focused on data contracts, source to target integration with Merlin and enterprise systems, APIs, event integration, document and metadata flows, controlled write back, data quality, lineage, and operational support. This role holds end to end accountability for data and integration delivery, including the batch, service oriented, and legacy interface dependencies that AI enabled underwriting workflows rely on.
How you will contribute:- Design, build, test, and operate the data and integration components for AI enabled mortgage workflows, including source ingestion, transformation, enrichment, document metadata, reference data, APIs, event flows, exception handling, and controlled write back to core systems.
- Hold end to end integration accountability for AI use cases across Merlin, SQL Server sources, document repositories, enterprise services, and event backbones, so that integration delivery does not fragment across AI, product, data, and architecture teams.
- Translate workflow, policy, reporting, and operational requirements into source to target mappings, canonical data models, interface contracts, validation rules, reconciliation controls, and traceable data lineage, in partnership with the Merlin Product Owner, the Residential Underwriting Business SME, AI Engineers, and application teams.
- Build governed batch and near real time pipelines using approved First National patterns across Databricks and Azure, including Lakeflow or comparable orchestration, Delta Lake bronze, silver, and gold layers, Unity Catalog, SQL Server, document repositories, and enterprise integration services.
- Develop and maintain secure APIs, services, and event integrations using Python, SQL, FastAPI, .NET or comparable technologies, Azure Service Bus, API Management, Git based workflows, automated tests, CI/CD, and infrastructure as code practices.
- Implement data quality and integration controls, including schema validation, completeness, accuracy, timeliness, duplication, referential integrity, reconciliation, error routing, replay, idempotency, observability, alerting, and audit evidence.
- Apply privacy, security, retention, access control, and data governance requirements to structured and unstructured information, including PII handling, Entra ID, RBAC, Key Vault, private connectivity, logging, and least privilege access.
- Support AI Engineers and full stack engineers with reliable, documented interfaces and representative data, diagnose data and integration failures through development, evaluation, UAT, parallel runs, production releases, incidents, and ongoing monitoring, and refactor partner delivered or prototype pipelines into modular, testable, documented, supportable services, contributing reusable connectors, data contracts, mappings, and engineering patterns back to the AI Factory catalog.
The experience you need:- Bachelor's degree in computer science, software engineering, data engineering, information systems or a related discipline.
- 5 plus years of progressive data engineering, integration engineering, software engineering, cloud engineering, or related technology experience.
- Strong hands on experience with Python, SQL, APIs, ETL or ELT pipelines, data modeling, automated testing, Git based delivery, CI/CD, cloud services, and production support.
- Practical experience with Microsoft Azure, Databricks or comparable data platforms, Delta Lake, Unity Catalog, Lakeflow or comparable orchestration tools, SQL Server, Azure Service Bus, API Management, Key Vault, Entra ID, and infrastructure automation is strongly preferred.
- Experience designing data contracts, source to target mappings, canonical models, event driven interfaces, change data capture, reconciliation, lineage, metadata, data quality controls, and controlled write back to enterprise systems, including integration with legacy, batch, and service oriented interfaces.
- Ability to build resilient integrations with attention to performance, scalability, security, privacy, governance, error handling, replay, idempotency, observability, cost, and operational support.
- Experience collaborating with product owners, domain experts, AI and application engineers, platform teams, enterprise data teams, security, QA, and operations through design, UAT, production release, incident response, and continuous improvement.
- Experience in financial services, mortgage lending, lending operations, servicing, broker channels, third party partnerships, or regulated technology environments is preferred.
Relationships:External: Engages implementation partners, cloud providers, data and AI platform providers, and integration and specialist vendors on interface design, delivery quality, monitoring, production readiness, and knowledge transfer to First National.
Internal: Reports to the AI Engineering Lead within the AI Platform Squad and works closely with the Applied AI Platform Engineer, the AI Quality and Evaluation Lead, and the Technical Product Manager, AI Platform.
Working Environment and Physical Demands Analysis:- Office environment
- Periods of high volume with tight timelines
- Long periods of stationary position/sitting
- Prolonged periods of repetitive movement (i.e. using a keyboard and mouse)
- Long periods of time in viewing a computer screen
- Multi-tasking may include speaking to customers on a telephone call while looking up information on a computer program.
