Principal Enterprise Solutions Architect

This is a high-impact, customer-facing architecture role. The successful candidate will serve as a technical visionary and strategic partner to customer engineering leadership while remaining hands-on with architecture, design, engineering standards, and delivery.

The ideal candidate combines strong software engineering fundamentals, modern data architecture expertise, cloud-native experience, and excellent customer-facing communication skills., * Bachelor’s or master’s degree in computer science, Software Engineering, or a highly related technical field. \n

• Overall Professional Experience: Minimum of 12+ years of hands-on experience in software engineering, data platform engineering, and enterprise systems. \n

• Architectural Leadership: 5+ years of experience in a Principal or Lead Architect role, with a proven track record of designing, deploying, and maintaining production-grade enterprise software architectures. \n

• Customer-Facing Technical Liaison: 3+ years of experience interacting directly with client stakeholders (Directors, VPs, and Technical Leads) to share architectural vision and translate business requirements into technical specs. \n

• Core Engineering Stack: 8+ years of deep hands-on development experience in Java, Python, and advanced SQL. \n

• Modern Data Pipelines & Streaming: 5+ years of hands-on experience with streaming/messaging systems (Kafka) and data orchestration tools (Airflow, dbt). \n

• Cloud & DevOps Ecosystems: 5+ years of experience architecting on public cloud environments (with a strong preference for AWS), utilizing containerization (Kubernetes, Docker, AWS EKS) and Infrastructure as Code (Terraform). \n

• AI-Led Engineering (Realistic Market Threshold):1-2 years of practical experience integrating LLM patterns (e.g., LangChain, vector databases) and leveraging AI-assisted coding tools (e.g., GitHub Copilot, Cursor) within the SDLC. \n, n \n

• Experience modernizing legacy enterprise data platforms. \n

• Strong understanding of event-driven and distributed systems. \n

• Experience with data lakes, data platforms, streaming architectures, and cloud-native applications. \n

• Experience working with open-source technology ecosystems. \n

• Experience in customer-facing consulting or technology advisory roles. \n

• Experience working in large enterprise transformation programs. \n

• Experience with AI/GenAI adoption within enterprise engineering organizations.

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• Technical Customer Liaison: Act as the primary technical partner for the customer’s engineering leads, effectively communicating architectural vision, aligning technical Roadmaps, and building strong cross-functional relationships. \n

• Architectural Governance: Establish and champion the enterprise-wide technical standards, design templates, and architecture frameworks. Organize and lead the internal Architecture Community of Practice (CoP) sessions. \n

• Technology Evaluation: Continuously evaluate emerging open-source technologies, cloud services, and AI frameworks to recommend adoption strategies that optimize scalability, reliability, and cost. \n

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Data Architecture & Engineering

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• Pipeline Modernization: Lead large-scale pipeline re-architecture initiatives, converting traditional batch ETL processes into real-time, event-driven architectures using Change Data Capture (CDC) and Kafka Connect. \n

• Ecosystem Design: Architect, scale, and optimize data lakes and pipelines utilizing modern open-source stacks including Airflow, AWS EKS, PostgreSQL, and specialized search/vector databases. \n

• Data Governance & Quality: Enforce enterprise-wide schema cataloging using tools like Kafka Schema Registry. Ensure data quality, security, compliance, and governance standards are built natively into all pipelines. \n

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Cloud-Native & AI-Led SDLC

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• System Integration: Design robust, scalable, and maintainable Java- and Python-based backend architectures, ensuring seamless integration with data pipelines, APIs, and cloud infrastructure. \n

• AI-Infused Engineering Productivity: Drive the adoption of AI tools (e.g., GitHub Copilot, Cursor) and LLM-driven test/review generation frameworks to maximize team delivery speed and software quality. \n

• Spec-Driven Development: Enforce a disciplined development model: Requirements * Specifications * User Stories * Test Cases * Code * CI/CD Deployment. \n

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Delivery Discipline & Operations

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• Engineering Rigor: Push engineering teams, project managers, and product owners toward stronger delivery discipline by implementing strict design validation, rigorous code reviews, automated unit/integration testing, and release governance. \n

• Platform Reliability & Cost Optimization: Proactively troubleshoot and resolve complex, high-priority production incidents. Support ongoing capacity planning and cloud infrastructure cost optimization. \n

• Agile Collaboration: Lead and participate in agile ceremonies (scrums, reviews, retrospectives), providing clarity on technical blockers, achievements, and technical debt. \n

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