Data & Backend Architect – Azure Data Brick, Data Layer (Claude AI–Driven)
Product Development, Sustenance, Modernization & Agentic AI infusion
Travel: 25% during active client engagements
Experience Level: 15+ years

Key Responsibilities
Architect and build the core backend systems that power cross-portfolio data normalization, scalable reporting layers, and analytics-ready data platforms.
Design and implement backend services, APIs, and data pipelines using an Azure-based stack (Azure Web Apps, Microsoft Fabric), ensuring high performance, scalability, and production readiness.
Structure and deliver data models and transformation pipelines that standardize disparate portfolio company data into a consistent, queryable format for dashboards and downstream analytics
Enable natural-language and ad hoc analytics capabilities by building backend systems that support AI-driven querying over normalized enterprise data.
Leverage Claude AI extensively as an engineering productivity accelerator—driving faster GTM through rapid prototyping, code generation, debugging, and design exploration across the development lifecycle.
Establish and scale Claude-driven development practices to improve engineering velocity, reduce cycle times, and enhance code quality and consistency within the embedded team.
Own backend technical design for platform evolution, translating product requirements into robust, scalable backend implementations in close collaboration with the client’s product team.
Operate as a fully embedded engineer within the client team, contributing to day-to-day development, design reviews, and delivery workflows aligned to a dedicated, full-time engagement model.
Maintain high engineering standards including code quality, testing rigor, modular architecture, and maintainability across all backend deliverables.
Drive execution discipline and delivery outcomes, ensuring backend systems are production-grade, aligned with roadmap timelines, and optimized for rapid iteration enabled by Claude AI.
Required Qualifications
15+ years of software engineering experience, including 5+ years at Principal Architect / Senior IC level building large-scale backend and data platforms in enterprise environments.
Proven experience designing and delivering data-intensive backend systems that support reporting, analytics, and multi-source data normalization at enterprise scale.
Demonstrated ownership of backend architecture and delivery for at least two large platform programs with measurable outcomes (scalability, performance, usability, or time-to-market improvements).
Active hands-on coder, currently writing production-grade backend code, reviewing pull requests, and driving reference implementations.
Strong experience working in embedded/forward-deployed engineering models, collaborating directly with product teams and stakeholders to translate evolving requirements into backend systems
Extensive usage of Claude AI or equivalent AI coding assistants as a core development accelerator—demonstrated impact on engineering velocity, code quality, and GTM timelines.
Backend & Data Platform Engineering (Required — Primary Focus Area)
Strong expertise in backend development using modern service-oriented and API-driven architectures (REST, gRPC, event-driven patterns).
Experience designing data ingestion, transformation, and normalization pipelines for heterogeneous enterprise data sources.
Deep understanding of scalable data modeling for reporting, dashboards, and analytics consumption layers.
Proven ability to build backend systems that enable ad hoc querying and analytics over structured datasets.
Expertise in distributed systems design, including fault tolerance, performance optimization, and scalability patterns.
Cloud & Azure Stack (Required — Primary Focus Area)
Hands-on experience with Azure-based backend systems, including App Services / Web Apps and cloud-native service design.
Experience working with modern cloud data platforms, including Microsoft Fabric or equivalent (data lake, warehouse, transformation pipelines).
Familiarity with building backend systems that integrate with AI and analytics layers, supporting natural-language or advanced querying use cases.
Strong understanding of cloud-native observability, deployment patterns, and performance tuning.
Claude AI–Driven Engineering (Required — Differentiator)
Extensive hands-on usage of Claude AI for software development, including:
Code generation and rapid prototyping
Debugging and optimization
System design exploration
Documentation and knowledge capture
Demonstrated ability to accelerate GTM timelines using Claude AI, reducing development cycle times while maintaining high quality.
Experience establishing Claude-driven development workflows and best practices within engineering teams.
Ability to balance AI-assisted development with strong engineering judgment, ensuring maintainable, production-grade code.
Engineering Excellence & Delivery (Required)
Strong grounding in engineering best practices: code quality, modular architecture, testing discipline, and maintainability
Experience delivering in time & materials or roadmap-driven engagements, adapting to evolving scope and priorities
Excellent problem-solving and system design skills, with the ability to operate independently in high-ownership IC roles
Strong communication skills, including working with product managers and stakeholders to shape technical solutions
Microsoft Fabric & Analytics Layer (Required)
Experience with Microsoft Fabric (OneLake, Lakehouse, Warehouse, data pipelines) for building analytics-ready platforms
Understanding of reporting and dashboarding ecosystems (e.g., Power BI or equivalent)
Awareness of designing data platforms optimized for downstream analytics and ad hoc insights generation
• Preferred Qualifications Microsoft certifications relevant to data and cloud platforms: Azure Solutions Architect Expert, Azure Data Engineer Associate, Azure AI Engineer Associate, Microsoft Fabric Analytics Engineer or equivalent.
• Demonstrated use of Claude AI (or equivalent AI coding assistants) to accelerate software delivery, with proven impact on reducing development cycle times and improving engineering productivity.
• Contributions to the engineering community, such as conference speaking, open-source contributions, or technical writing in backend systems, data platforms, or AI-enabled development practices.
• Industry experience in one or more of the following domains: Private Equity portfolio platforms, financial services, enterprise SaaS, or data-driven digital products.
• Experience working on data platform initiatives for multi-entity or portfolio-based organizations, including standardization and normalization of distributed data sources.
• Exposure to analytics-driven product platforms, including systems powering dashboards, reporting, and ad hoc insights generation.
• Experience operating in high-velocity, embedded delivery models, including partnerships with product teams, evolving roadmaps, and rapid GTM expectations.

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