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.

Key Responsibilities

Data Architecture & Financial Engineering

- 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

AI & Intelligent Analytics Enablement

- 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

Team Leadership & Development

- 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)

Delivery & Platform Engineering

- Oversee end-to-end design, build, and support of the FDR platform - Manage delivery timelines and cross-functional workstreams - 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

Regulatory, Controls & Governance

- 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

Stakeholder Engagement & Process Improvement

- 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

Qualifications

Education

- 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 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

Technical Skills

Core Technologies - Snowflake, Spark, Microservices - Data pipelines (ETL/ELT) and cloud data platforms - Enterprise data architecture (data lake → curated → consumption layers) AI/ML & Data Science - LLM orchestration frameworks (e.g., LangChain) - 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) Financial Systems - Experience with Treasury platforms (e.g., QRM) Finance & Treasury Domain Knowledge - 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 stress testing - Financial instruments (loans, deposits, derivatives, securities)

Leadership & Soft Skills

- Strong communication skills with ability to translate technical and financial concepts - Proven ability to manage multiple priorities and projects - Experience collaborating across Finance, Risk, Technology, and Data teams - Strong analytical and problem-solving capabilities

Work Schedule

Hours: 40 per week Schedule: Monday – Friday Work Model: Hybrid (4 days onsite, 1 day remote)