Data Architect

<p></p> <p></p> <h3><span data-olk-copy-source="MessageBody">Data Architect</span></h3> <h3>The Role</h3> <p>We are building our enterprise data platform from the ground up and need a <strong>Data Architect</strong> to own it.</p> <p>This is a <strong>greenfield, hands-on leadership role</strong> — not a consulting engagement. You will define the architecture, make the technology decisions, build the foundation, and be accountable for outcomes. You will report directly to the CTO and partner closely with a Senior Data Engineer on the same hiring cycle.</p> <p>The platform will serve business analytics, operational reporting, and AI-driven capabilities across multiple markets and enterprise clients. A key deliverable is enabling AI applications and agents to consume trusted enterprise data securely via APIs and <strong>Model Context Protocol (MCP)</strong>.</p> <h3>What You Will Own</h3> <ul> <li><span><strong>Data platform architecture</strong> — Data Lake, Lakehouse, semantic layers, and data consumption patterns across structured, semi-structured, and event-based sources.</span></li> <li><span><strong>Ingestion and transformation pipelines</strong> — batch, streaming, and CDC-based, with proper orchestration, observability, and failure handling.</span></li> <li><span><strong>Data modelling</strong> — scalable analytical models covering core business domains: orders, inventory, fulfilment, logistics, marketplaces, billing, and platform performance.</span></li> <li><span><strong>Business analytics</strong> — governed KPI definitions, dashboards, and self-service capabilities that replace manual reporting.</span></li> <li><span><strong>AI data enablement</strong> — architecture for exposing authoritative, governed data to AI agents through MCP and APIs, with appropriate access controls and tenant isolation.</span></li> <li><span><strong>Data governance and compliance</strong> — data quality, lineage, PII classification, and controls that meet enterprise security and privacy obligations across multiple jurisdictions.</span></li> <li><span><strong>Platform reliability</strong> — monitoring, SLAs, incident management, and operational runbooks so the platform runs as a production service.</span></li> </ul> <h3>What We Expect</h3> <p><strong>First 90 days:</strong></p> <ul> <li><span>Weeks 1–4: Assess the data landscape, produce an enterprise architecture proposal.</span></li> <li><span>Weeks 5–8: Deliver the first production pipeline and a priority BI dashboard.</span></li> <li><span>Weeks 9–12: Define common KPI models for two business domains and deliver the first MCP-based data capability for an AI agent.</span></li> </ul> <p><strong>6–12 months:</strong></p> <ul> <li><span>Production data platform operational with automated pipelines for priority datasets.</span></li> <li><span>Governed business models and trusted KPI definitions in active use by the business.</span></li> <li><span>Dashboards live and replacing manual reporting.</span></li> <li><span>Architecture for secure AI data consumption implemented, with initial MCP capabilities in production.</span></li> <li><span>Platform operational practices — quality, lineage, monitoring, cost controls — established and running.</span></li> </ul> <h3