Product Architect — Data Driven Workflows
Location: Remote (US) with periodic travel
Duration: 12 Months of Contract to Hire
About the Role
This is a hands-on Product Architecture role focused on designing and governing data-intensive financial systems across the product portfolio. You will define domain models, API and event contracts, and scalable workflows that support high-volume transactions, complex financial processes, and strict compliance requirements — while embedding AI capabilities safely into data quality and workflow automation.
This is a domain ownership role with real accountability. Your designs and contracts will be adopted across teams. Success is measured by your ability to handle large-scale financial data, ensure accuracy and auditability, enable reliable downstream adoption without breaking changes, and govern AI-assisted workflows with the rigor that financial systems demand.
What You Will Do
Product Architecture Ownership
Own product-level architecture for financial systems — domain models, system flows, and integration patterns supporting large-scale data and transaction processing
Define NFRs across latency, throughput, correctness, availability, operability, and security — with measurable acceptance criteria and live observability dashboards
Design API-first and event-driven architectures enabling scalable, reliable consumption across downstream systems
Establish data governance, auditability, and traceability standards across all financial workflows
Coach delivery teams on contract design, observability, and data integrity best practices
Financial Systems & Workflow Design
Architect solutions handling high-volume financial data, complex calculations, and multi-step workflows
Design systems supporting compliance-heavy processes — audit trails, regulatory reporting, and reconciliation
Define where business logic, enrichment, and validation reside — pipeline versus service layer — with idempotent processing, replayability, and recoverability for financial transactions
Partner with integration teams to design scalable, loosely coupled systems using event-driven patterns
AI-Assisted Data Quality & Workflows
Apply AI capabilities safely for data mapping, normalisation, and anomaly detection — scoped as decision-support with human approval, never as autonomous mutation of financial data
Design AI-assisted import workflows — covering CSV and Excel ingestion, intelligent column mapping, and multi-stage validation — with explicit evaluation criteria and safety gates that make AI assistance trustworthy, not just convenient
Design evaluation and safety gates for all AI-assisted flows to ensure outputs are auditable, correctable, and compliant with financial governance standards
Build rapid spikes and POCs to de-risk agent workflows, retrieval and evidence patterns, and performance assumptions; document all architectural decisions via ADRs
Data Integrity, Governance & Compliance
Design for accuracy, consistency, and auditability of financial data across systems
Implement governance frameworks ensuring compliance with financial regulations and internal controls
Define reconciliation strategies, exception handling, and correction workflows
Establish monitoring and alerting for data quality SLIs
POC Execution & Technical Leadership
Build POCs and technical spikes to validate architecture decisions around data processing, workflows, and integrations
Translate ambiguous financial and business requirements into scalable, well-documented technical designs
Document all architectural decisions via ADRs and maintain traceability across systems
Outcomes & Measures
Scalable architecture supporting high-volume financial data and complex workflows delivered and adopted across teams
Measurable improvements in data accuracy, processing reliability, and system performance
Domain models and contracts adopted by multiple downstream systems without breaking changes
AI-assisted workflows operational with measurable accuracy, human-approved safety gates, and audit-ready outputs
Robust audit, reconciliation, and recovery mechanisms in place with live observability dashboards
Required Qualifications
8+ years in software engineering with 3+ years in product or application architecture
Strong experience in fintech or financial systems involving large-scale data processing and complex workflows
Hands-on experience designing systems with high data volumes, transactional integrity, and compliance requirements
Proven ability to design data models, APIs, and event-driven systems for cross-team adoption with backward compatibility
Experience with governance, auditability, and regulatory considerations in system design
Strong understanding of NFRs — correctness, reliability, scalability, and observability — with measurable acceptance criteria
Ability to translate ambiguity into clear, buildable, well-documented architecture
Preferred Qualifications
Domain experience in payments, banking, trading systems, billing, licensing, entitlements, or financial platforms
Kafka / Confluent — schema governance, consumer patterns, replay strategies, and event streaming at scale
Experience integrating ERP or financial systems (SAP or equivalent) into downstream SaaS provisioning or reporting flows
Experience applying AI/ML for anomaly detection or data quality in controlled, auditable, human-approved loops
Cloud experience (Azure preferred) — AKS, storage, networking, monitoring — with distributed system design
Familiarity with Microsoft Foundry Agent Service / MAF or equivalent AI orchestration tooling
Core Competencies
Systems thinking — design scalable, resilient architectures for complex financial workflows under real constraints
Data integrity focus — correctness, auditability, and compliance-first mindset at every design decision
AI pragmatism — evaluation loops, safety gates, and human approval as non-negotiable design requirements; not the assumption that the model gets it right
Strong communication — equally effective with technical engineering teams and non-technical business stakeholders
Pragmatic architecture — balance scalability, governance, and delivery speed without sacrificing financial data accuracy
Design Patterns
Tools & Environment
Languages: C# / .NET (primary); Python / TypeScript (POCs and evaluation harnesses)
Cloud: Azure — AKS, Key Vault, Blob/ADLS, App Gateway, Monitor/Log Analytics
CI/CD: Azure DevOps or GitHub Actions; Bicep / Terraform
Eventing: Kafka / Confluent — schema governance and contract testing
Diagrams & Docs: Mermaid + C4; ADRs in /docs/adr
Data Stores: Relational and distributed systems supporting large-scale financial data
AI Orchestration: Foundry Agent Service / MAF or equivalent where appropriate
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