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.