Solution Engineering Manager- Financial Data Repository

Job Summary

We are seeking a Solution Engineering Manager to lead engineering efforts supporting the Finance Data Repository (FDR), the enterprise data backbone powering Treasury, Finance, and Regulatory analytics.

This role combines strategic leadership, technical expertise, and deep financial domain knowledge to deliver scalable, regulatory-compliant, and AI-enabled data solutions. The ideal candidate will bridge Finance stakeholders and engineering teams, ensuring solutions align with enterprise objectives while meeting regulatory standards. You will oversee end-to-end solution delivery—from requirements gathering through architecture, engineering, AI enablement, and production support—and mentor and develop a high-performing team.

• Establish authoritative enterprise data models for financial and regulatory reporting
• Align Finance data domains to support analytics, reporting, and AI‑driven decisioning
• Translate financial concepts into scalable data models and engineering solutions
• Design and build data pipelines across Finance, Treasury, Insurance, and Tax
• Develop governed semantic layers supporting KPI consistency, feature reuse, and NLP/NLQ access

• Enable AI use cases including:
  • Natural Language Query (NLQ) / Conversational Analytics
  • ML‑driven data quality monitoring and anomaly detection
  • RAG pipelines for document intelligence (regulatory docs, contracts)
  • AI‑ready feature engineering and governance
  • Agentic workflows for automation and reconciliation
• Partner with GenAI and AI Excellence teams to align with enterprise AI roadmap

• Lead and mentor data and solution engineering teams
• Foster a culture of technical excellence and continuous improvement
• Manage resource allocation, capacity planning, and workload prioritization
• Upskill team on AI/ML engineering (LLMs, embeddings, vector search, feature stores)

• Oversee end-to-end design, build, and support of the FDR platform
• Manage delivery timelines and cross-functional work streams
• Lead integration with:
  • Treasury systems (QRM)
  • BI tools (Power BI, Tableau)
  • AI/ML platforms and LLM frameworks
• Drive modernization to cloud‑native and automated architectures
• Build API‑driven and event‑based integrations

• Deliver regulatory‑grade datasets for internal and external reporting
• Implement data lineage, reconciliation, and validation frameworks
• Ensure compliance with SOX, RDAR, and audit requirements
• Establish monitoring for data quality, anomaly detection, and traceability
• Ensure AI outputs are explainable, auditable, and compliant

• Partner with Finance, Treasury, Tax, and Technology teams
• Present solutions to senior leadership and regulatory stakeholders
• Support vendor evaluations and solution demonstrations
• Continuously improve engineering processes and tools
• Stay current on industry trends, AI/ML advancements, and data platforms

• Bachelor’s degree in Engineering, Computer Science, Finance, or related field

• 10+ years in data engineering, platform engineering, or financial systems integration
• 5+ years in a leadership role managing engineering teams
• Proven experience delivering complex financial data platforms in regulated environments
• 2+ years hands‑on experience with AI/ML engineering or data science infrastructure

• Snowflake, Spark, Microservices
• Data pipelines (ETL/ELT) and cloud data platforms
• Enterprise data architecture (data lake → curated → consumption layers)

• LLM orchestration frameworks (e.g., Lang Chain)
• Vector databases (FAISS, Pinecone)
• ML‑based anomaly detection and data quality frameworks
• Semantic data layer design for AI/ML and NLQ
• Cloud AI platforms (AWS Bedrock, Azure OpenAI)

• Experience with Treasury platforms (e.g., QRM)

• ALM, FTP, liquidity management, cash flow modeling
• Interest rate risk (yield curves, repricing, spreads)
• Regulatory reporting and capital frameworks (RWA, Economic Capital)
• FR 2052a and liquidity…