Role Overview

Macnaught is building an AI-First Analytics Solution: a single, trusted analytics layer across Business Central (ERP), HubSpot (CRM), Shopify and Amazon (D2C/LNL), spanning operations, sales, and customer success data across Australia, NZ, the US, and India. This role is a key member of the team building that solution end to end: from modelling and transforming raw data into reliable business metrics, through to the semantic layer, and eventually the natural-language / AI-prompt layer that lets leaders and managers ask questions directly of the data.

This is a combined Analytics Engineer + Visualization Engineer role, and the person will be one of the core technical builders on this solution long-term, alongside an Analytics Architect and a Functional SME who bridges department context.

Important: the AI-First Analytics Solution will take time to build. Until it is live, this person is directly responsible for keeping the business running on interim outputs: building and maintaining Power BI dashboards and, where a dashboard isn't yet practical, Excel-based reports, for whichever business stakeholders need them. This interim delivery work is a core and ongoing part of the role.

Key Responsibilities

- Act as a key team member on Macnaught's AI-First Analytics Solution: help build the data extraction, transformation, semantic, and (eventually) AI-prompt layers as the solution is rolled out in phases.
- Until that solution is fully live, deliver interim outputs directly to business stakeholders: Power BI dashboards where practical, Excel-based reports where a dashboard isn't yet the right tool, so decisions don't wait on the longer-term build.
- Design and build data models that turn raw source data (Microsoft Business Central, HubSpot, Shopify, Amazon) into trusted business tables: brand-level P&L;, channel profitability, SKU-level margin, inventory ageing.




- Define and maintain metric logic in a semantic layer (e.g. contribution margin, inventory at risk, customer LTV) so the same number means the same thing everywhere it appears.
- Own the Power BI environment: data modelling (star schemas), DAX measures, Power Query transformations, and dashboard design that non-technical managers can navigate unassisted.
- Translate business questions from department heads (operations, sales, customer success) into correct queries and visuals, working with the Functional SME where domain context is unclear.
- Monitor data pipeline health (sync failures, schema changes from source systems) and flag issues before they reach a dashboard or report.
- Maintain documentation for metric definitions, data lineage, and dashboard logic so the system remains explainable and auditable.
- Support the phased roadmap toward natural-language / AI-prompt analytics as that layer is introduced.

Required Experience & Skills

- 6-12 years of experience in an analytics engineering, BI development, or data analytics role, ideally in a manufacturing, distribution, or multi-channel B2B/D2C environment.
- Strong SQL - non-negotiable. Comfortable writing and troubleshooting complex queries independently.
- Excellent, hands-on experience with Airbyte (or a similar data extraction/sync tool) for pulling data from source systems on a schedule.
- Excellent, hands-on experience with dbt for data transformation and modelling; comfortable owning a dbt project,



not just writing individual models.
- Excellent, hands-on experience with Metabase (or a comparable BI/semantic tool) alongside Power BI.
- Advanced Power BI: DAX, data modelling, Power Query, and a portfolio of dashboards that were actually adopted by business users (not just built and shelved).
- Robust Excel skills for interim, non-dashboard reporting: able to produce clean, reliable, stakeholder-ready reports quickly when a full dashboard isn't the right tool yet.
- Some exposure to a cloud data warehouse (Snowflake, BigQuery, Azure Synapse, or similar).
- Demonstrated ability to work directly with business stakeholders: can ask the right clarifying questions on a vague business problem rather than requiring a fully written spec.
- Experience reconciling data across multiple systems (ERP, CRM, e-commerce) and resolving conflicting definitions of the same metric.
- Very Good Spoken English.

Nice to Have

- Exposure to Microsoft Business Central, HubSpot, Shopify, or Amazon Seller data.
- Familiarity with AI-assisted BI tools (Power BI Copilot, or similar natural-language query layers).

What Success Looks Like in the First 612 Months

- Business stakeholders are getting reliable, timely Power BI dashboards and/or Excel reports in the interim, while the AI-First Analytics Solution is still being built.
- Core financial and operational metrics (brand P&L;, channel profitability, SKU margin, inventory ageing) are modelled once, governed centrally, and no longer debated across teams.
- Department heads across AU, NZ, US and India are using self-serve Power BI dashboards for their own reporting, reducing ad hoc reporting requests to the analytics function.
- A documented semantic layer exists so metric definitions are consistent and explainable to any new stakeholder.

📌 Analytics Engineer (Gurugram)
🏢 Macnaught
📍 Gurugram