Senior Data Integration Engineer

Job Title: Senior Data Integration Engineer Department: Information Technology FLSA Status: Exempt Reports to: Vice President, Director of Artificial Intelligence Senior Data Integration Engineer at Chain Bridge Bank, N.A. The Senior Data Integration Engineer will own the bank's approved knowledge and systems layer for AI by implementing secure grounding on enterprise content, approved connectors, permissions-aware retrieval, and DLP-aligned data handling. This role includes the immediate masking and unmasking path for SharePoint-accessed content using Presidio and Protect. Compensation Compensation will be commensurate with experience and qualifications. This role is eligible to participate in the Bank's annual incentive compensation plan, under which employees may earn a cash bonus based on the performance of the Company and the team, with an emphasis on financial results and risk management. Plan terms, eligibility, and payout amounts are determined at the sole discretion of the Bank and are subject to change. This is a hybrid position, with a requirement to be in the office at least three days per week in McLean, Virginia. Benefits Chain Bridge offers a professional benefits package consistent with a regulated banking environment, including: • Competitive base salary • Comprehensive health benefits (medical, dental, and vision) • 401(k) retirement plan with employer contribution • Paid time off, including vacation, holidays, and sick leave • Annual incentive compensation plan • Professional development opportunities relevant to the role and the Bank's operating environment Responsibilities: Core Responsibilities • Build and maintain secure grounding patterns for approved content repositories and internal business systems, starting with SharePoint and other governed enterprise sources. • Implement approved connectors and retrieval workflows with permissions-aware access, source traceability, and controls that scope retrieval to the minimum necessary data. • Implement and support Presidio and Protect for immediate masking and unmasking of SharePoint-accessed content before it is released to approved AI workflows. • Partner with Security, IT, Information Governance, and business owners on DLP, sensitivity labels, metadata, redaction, and data-handling controls for AI retrieval paths. • Improve retrieval quality, metadata standards, and integration reliability across structured and unstructured sources so answers remain grounded and supportable. • Create reusable onboarding standards for new content sources, including access review, logging expectations, retention considerations, and validation before activation. • Engineer retrieval services that preserve permissions inheritance and return provenance metadata sufficient to support reviewer verification, citations, and audit traceability. • Build validation checklists for new repositories, connectors, content types, and SharePoint masking flows before they are exposed to employee-facing copilots or higher-risk governed workflows. • Partner with Guardrails and Application teams to tune retrieval quality, reduce hallucination risk, and enforce least-privilege data access across approved workflows. • Support secure integration patterns for approved enterprise AI platforms, including Claude Cowork and related retrieval-dependent tools, where grounding, connector behavior, provenance controls, and masking services must be enforced. Control Requirements • Implement connector and retrieval logging that supports audit trails for what data sources were accessed, by whom, and under what approved workflow, where the platform supports it. • Design integrations to respect data minimization, permissions inheritance, read-only access where required, and restrictions on shared file write or delete behavior. • Help operationalize immediate compensating controls for PII or NPI workflows, including Presidio and Protect for SharePoint-accessed content, with documented fallback redaction controls and QA where needed. • Coordinate evidence and metadata standards so approval artifacts, retrieval traces, and content-source onboarding records can be retained in the governance repository. • Contribute technical review for plugins, custom MCP servers, and other integrations that expose enterprise systems to AI workflows. Qualifications: Required • 6+ years in data engineering, integrations, enterprise search, retrieval engineering, knowledge systems, or content-platform engineering. • Hands-on experience with APIs, enterprise content platforms, permissions models, identity-aware retrieval, and reliable integration patterns across heterogeneous data sources. • Strong understanding of data classification, DLP concepts, metadata, lifecycle, and retention considerations, and enterprise content governance. • Ability to troubleshoot retrieval quality, indexing, connector reliability, and source traceability in document-heavy environments. • Experience documenting standards so new data sources can be onboarded repeatedly without creating inconsistent control behavior. Preferred • Experience with SharePoint, Microsoft Graph, Copilot Studio, grounding patterns, semantic search, vector or hybrid retrieval, and enterprise content systems used in regulated environments. • Exposure to legal, trust, compliance, HR, or document-heavy operational processes where permissions and provenance matter. • Financial services or other regulated data experience with practical awareness of privacy, records, and audit obligations. • Hands-on experience with Presidio, Protect, or comparable masking, redaction, and data-protection tooling that can support controlled AI workflows. • Experience supporting Claude Cowork or similar enterprise AI platforms where retrieval, knowledge access, and provenance controls matter. • Familiarity with Claude Code or comparable AI-assisted engineering tools for connector development, debugging, and integration acceleration. Compliance It is understood that complying with all applicable safety and soundness and consumer compliance laws and regulations, taking the annually required consumer compliance courses, and adhering to the policies and procedures that facilitate compliance will all be factors considered when evaluating individual performance. Individual performance is rewarded in annual salary adjustments. Bank compliance with laws and regulations is a factor considered in the calculation of incentive compensation. The ratings that the Bank receives from its regulators and its auditors are factored into the annual incentive compensation calculation. Your adherence to these laws and regulations and the policies and procedures that support them directly affects the Bank's compliance. Annual incentive compensation rewards team performance. An employee will not be eligible for incentive compensation unless he/she takes the consumer compliance courses required of all employees and all the required consumer compliance courses for his/her job description or job responsibilities by the end of each calendar year. All required consumer compliance courses for the applicable year will be outlined in the Compliance Management Program.

• Core Responsibilities • Build and maintain secure grounding patterns for approved content repositories and internal business systems, starting with SharePoint and other governed enterprise sources. • Implement approved connectors and retrieval workflows with permissions-aware access, source traceability, and controls that scope retrieval to the minimum necessary data. • Implement and support Presidio and Protect for immediate masking and unmasking of SharePoint-accessed content before it is released to approved AI workflows. • Partner with Security, IT, Information Governance, and business owners on DLP, sensitivity labels, metadata, redaction, and data-handling controls for AI retrieval paths. • Improve retrieval quality, metadata standards, and integration reliability across structured and unstructured sources so answers remain grounded and supportable. • Create reusable onboarding standards for new content sources, including access review, logging expectations, retention considerations, and validation before activation. • Engineer retrieval services that preserve permissions inheritance and return provenance metadata sufficient to support reviewer verification, citations, and audit traceability. • Build validation checklists for new repositories, connectors, content types, and SharePoint masking flows before they are exposed to employee-facing copilots or higher-risk governed workflows. • Partner with Guardrails and Application teams to tune retrieval quality, reduce hallucination risk, and enforce least-privilege data access across approved workflows. • Support secure integration patterns for approved enterprise AI platforms, including Claude Cowork and related retrieval-dependent tools, where grounding, connector behavior, provenance controls, and masking services must be enforced.Control Requirements • Implement connector and retrieval logging that supports audit trails for what data sources were accessed, by whom, and under what approved workflow, where the platform supports it. • Design integrations to respect data minimization, permissions inheritance, read-only access where required, and restrictions on shared file write or delete behavior. • Help operationalize immediate compensating controls for PII or NPI workflows, including Presidio and Protect for SharePoint-accessed content, with documented fallback redaction controls and QA where needed. • Coordinate evidence and metadata standards so approval artifacts, retrieval traces, and content-source onboarding records can be retained in the governance repository. • Contribute technical review for plugins, custom MCP servers, and other integrations that expose enterprise systems to AI workflows.