Data Architect

Data Architect The Role We are building our enterprise data platform from the ground up and need a Data Architect to own it. This is a greenfield, hands-on leadership role — 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. 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 Model Context Protocol (MCP). What You Will Own - Data platform architecture — Data Lake, Lakehouse, semantic layers, and data consumption patterns across structured, semi-structured, and event-based sources. - Ingestion and transformation pipelines — batch, streaming, and CDC-based, with proper orchestration, observability, and failure handling. - Data modelling — scalable analytical models covering core business domains: orders, inventory, fulfilment, logistics, marketplaces, billing, and platform performance. - Business analytics — governed KPI definitions, dashboards, and self-service capabilities that replace manual reporting. - AI data enablement — architecture for exposing authoritative, governed data to AI agents through MCP and APIs, with appropriate access controls and tenant isolation. - Data governance and compliance — data quality, lineage, PII classification, and controls that meet enterprise security and privacy obligations across multiple jurisdictions. - Platform reliability — monitoring, SLAs, incident management, and operational runbooks so the platform runs as a production service. What We Expect First 90 days: - Weeks 1–4: Assess the data landscape, produce an enterprise architecture proposal. - Weeks 5–8: Deliver the first production pipeline and a priority BI dashboard. - Weeks 9–12: Define common KPI models for two business domains and deliver the first MCP-based data capability for an AI agent. 6–12 months: - Production data platform operational with automated pipelines for priority datasets. - Governed business models and trusted KPI definitions in active use by the business. - Dashboards live and replacing manual reporting. - Architecture for secure AI data consumption implemented, with initial MCP capabilities in production. - Platform operational practices — quality, lineage, monitoring, cost controls — established and running. What We Are Looking For - 10+ years across data engineering, data platforms, or data architecture — with real architecture ownership, not just delivery. - Proven experience designing and building enterprise Data Lake, Warehouse, or Lakehouse platforms. - Strong SQL, data modelling, pipeline design, and cloud-native (preferably AWS) skills. - Experience with governance, data quality, lineage, and compliance — including PII and privacy controls. - Hands-on enough to validate designs and build in the early phase; structured enough to define standards that scale. - Experience in eCommerce, logistics, marketplace, or B2B SaaS is strongly preferred — the domain is complex and ramp time matters. Desirable: Practical experience with MCP, LLM/AI integration, semantic layers, RAG, or secure enterprise data access for AI systems. The Opportunity This is a founding role. The data platform does not yet exist. You will define what good looks like at Anchanto — and build it. If you are energised by greenfield architecture, comfortable with high ownership, and capable of moving fluently from business question to data pipeline to AI consumption — we want to talk.