TELECOMMUTE Product Architect -Data Driven Workflows_Remote

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, * 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

• 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