Job DescriptionWe are looking for a Solution Engineering Manager to lead a team of engineers in supporting
Finance Data Repository (FDR)-the enterprise data backbone powering Treasury, Finance, and Regulatory analytics. This role requires a strategic thinker with strong technical leadership and deep understanding of financial data architecture, Finance, Treasury and Tax functions, regulatory reporting requirements, and
modern AI/ML patterns applied to financial data. The ideal candidate bridges the gap between Finance business stakeholders and engineering teams, ensuring solutions are technically sound, regulatory-grade, and aligned with enterprise objectives. The candidate will oversee the end-to-end solution development process-from requirements gathering with Finance and Treasury users, through data model design, pipeline engineering, system integration,
AI feature enablement, and post-deployment support. They will work closely with Finance, Technology and vendor teams to ensure FDR solutions are feasible, scalable, and deliver value across the enterprise. They will also play a key role in mentoring and developing the team, fostering a culture of innovation, collaboration, and continuous improvement. The ability to communicate complex financial and technical concepts in a clear and compelling manner will be essential in building trust with business stakeholders, regulators, and engineering partners. Applicants should have a proven profile that combines
practitioner depth + cross-finance expertise + technical fluency + AI/ML engineering awareness and translates complex financial behavior into scalable, governed, intelligent data solutions. The Solution Engineering Manager will serve as the primary point of contact between the engineering team and business stakeholders across Finance, and Technology.
Key ResponsibilitiesData Architecture & Financial Domain Engineering- Establish authoritative data models enabling consistency across management and regulatory outputs
- Drive alignment between Finance data domains to support enterprise analytics, reporting, and AI-driven decisioning
- Translate financial concepts into data models and technical solutions; work closely with engineering and data teams
- Engineer data structures and pipelines supporting Finance, Treasury, Insurance, and Tax data
- Design and govern semantic data layers that enable consistent KPI definitions , governed feature reuse across AI/ML models, and natural language query (NLQ) access to finance data
AI & Intelligent Analytics Enablement- Architect the data foundation that powers AI use cases across Finance and Treasury, including:
- NLQ / Conversational Analytics: Enable natural language queries against FDR datasets (profitability, liquidity, balance sheet) so analysts can retrieve insights without SQL or analyst support
- ML-Driven Data Quality & Anomaly Detection: Implement machine learning models that proactively detect data anomalies, reconciliation breaks, and reporting errors before they reach production
- RAG Pipelines & Document Intelligence: Support retrieval-augmented generation (RAG) architectures that connect regulatory documents, loan agreements, and financial contracts to structured FDR data for AI-powered extraction and summarization
- AI-Ready Feature Engineering: Ensure FDR delivers consistent, governed feature definitions reduce feature drift across predictive models
- Agentic Workflows & Automation: Enable AI-driven workflow routing, automated reconciliation, and intelligent exception handling that reduce manual intervention in close cycles and regulatory reporting
- Partner with the GenAI Council and AI Excellence teams to align FDR data products with enterprise AI roadmap and use case prioritization
Team Leadership & Talent Development- Lead, mentor, and develop a team of data engineers and solution engineers building FDR platform components
- Foster a culture of technical excellence, accountability, and continuous improvement
- Manage capacity planning, resource allocation, and workload balancing across concurrent initiatives
- Upskill team on AI/ML engineering patterns, including vector search, embeddings, LLM integration, and governed feature stores
Delivery & Platform Engineering- Oversee end-to-end design, engineering, and support of the FDR platform
- Translate complex financial business logic into scalable, governed data engineering solutions
- Manage project timelines, resources, and deliverables across concurrent workstreams
- Lead integration with enterprise platforms including:
- Treasury modeling systems (QRM)
- BI/analytics tools (Power BI, Tableau)
- AI/ML serving layers and LLM orchestration frameworks
- Drive modernization from legacy platforms into cloud-native, automated architectures
- Build API-driven and event-based integrations supporting daily and monthly production cycles
Regulatory, Controls & Governance- Deliver regulatory-grade datasets supporting internal and external reporting
- Implement source-to-report data lineage, reconciliation logic, and validation frameworks
- Ensure all outputs meet SOX, RDAR, and audit expectations
- Implement within pipelines: data quality monitoring and anomaly detection / reconciliation frameworks (GL, sub-ledger, cross-system balance checks) / full metadata, lineage, and audit traceability
- Ensure AI model outputs are explainable, auditable, and aligned with regulatory expectations
- Align platform outputs to regulatory and internal control standards
Stakeholder Engagement & Process Improvement- Partner with Treasury, Finance, Tax, and Technology to define requirements, assess feasibility, and ensure scalability
- Present solutions and progress to senior leadership and regulatory stakeholders
- Support pre-implementation activities including vendor evaluation, competitive analysis, and solution demonstrations
- Continuously refine engineering processes, methodologies, and tools to enhance delivery efficiency, data quality, and platform reliability
- Stay current with industry trends, emerging technologies, AI/ML advancements, and cloud/enterprise data platforms
RequirementsEducation- Bachelor's degree in Engineering, Computer Science, Finance, or related field
Experience- 10+ years in data engineering, platform engineering, or financial systems integration
- 5+ years in a leadership or management position overseeing engineering teams
- Proven track record delivering complex, multi-system financial data platforms in regulated environments
- 2+ years hands-on experience with AI/ML engineering, data science infrastructure, or intelligent automation in a financial services context
Technical Expertise- Strong hands-on experience with:
- Snowflake, Spark, Microservices
- Data pipeline frameworks (ETL/ELT), cloud data platforms, large-scale data processing
- Enterprise data architecture patterns (data lake → curated layers → consumption)
- AI/ML Technical Proficiency:
- LLM orchestration frameworks (LangChain or equivalent)
- Vector databases and embedding stores (FAISS, Pinecone, or similar) for RAG and semantic search
- ML-driven data quality frameworks and anomaly detection models
- Semantic layer design for AI/ML feature consistency and NLQ enablement
- Familiarity with cloud AI services (AWS Bedrock, Azure OpenAI, or equivalent)
- Experience with Treasury or financial platforms (QRM)
Finance & Treasury Domain Knowledge- Strong working knowledge of:
- ALM, FTP, liquidity management, cash flow modeling
- Interest rate risk concepts (repricing, yield curves, spreads)
- RWA, Economic Capital, and regulatory reporting frameworks
- FR 2052a and capital/liquidity stress testing
- Financial product structures (loans, deposits, derivatives, investment securities)
Soft Skills & Leadership- Excellent communication and interpersonal skills-ability to translate complex technical, financial, and AI concepts for diverse audiences
- Proven ability to manage multiple projects and competing priorities simultaneously
- Experience working with cross-functional teams across Finance, Risk, Technology, and AI/Data Science organizations
- Strong problem-solving and analytical skills
Hours & Work Schedule- Hours per Week: 40
- Work Schedule: Monday - Friday
- Hybrid: 4 days per week onsite, 1 day remote
