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.

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