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.