Analytics Engineer

Role Overview

We are looking for a Senior Analytics Engineer to transform our data infrastructure from a reactive service model into a proactive, scalable engine. Currently, our data team manages a high volume of requests from cross functional teams. We need a specialist who can step in to centralize our business logic, design robust data structures in Redshift, and eliminate the manual "firefighting" that currently plagues our dashboards.

You will be the architect of our "Single Source of Truth," ensuring that when a metric changes, it updates everywhere, not just in one isolated dashboard.

Key Responsibilities

Data Modeling & Architecture: Transition logic out of Metabase SQL queries and into modular, version-controlled models within AWS Redshift.
Build for History: Design and implement historical tracking (e.g., Slowly Changing Dimensions) so we can accurately report on "Active Cards" or user states at any point in the past.
Pipeline Optimization: Work alongside our data engineers to optimize AWS Airflow DAGs, ensuring data is clean, tested, and reliable before it hits the BI layer.
Stakeholder Partnership: Act as the primary point of contact for Finance, CX, and Marketing to translate business requirements into technical specs.
Process Governance: Implement a public Kanban/Agile workflow to manage intake, prioritize high-impact requests, and provide transparency to leadership.
Dashboard Consolidation: Audit our existing Metabase environment to move away from "disposable" dashboards toward a curated set of "Gold Standard" executive tools.

Technical Requirements

Expert SQL: You don't just write queries; you write performant, readable, and dry (Don't Repeat Yourself) code.
dbt (Data Build Tool): Deep experience using dbt to manage the transformation layer is highly preferred.
Data Warehousing: Strong understanding of AWS Redshift (dist keys, sort keys, and query optimization).
The "Modern Data Stack": Experience with version control (Git), orchestration (Airflow), and BI tools (Metabase, Tableau, or Looker).
Dimensional Modeling: A firm grasp of Kimball or Star Schema methodology to create tables that make simple reporting... simple.

Details

Location

Dubai - United Arab Emirates

Experience

Min. 5 yearsExperienced

Technologies

Role Overview

You will be the architect of our "Single Source of Truth," ensuring that when a metric changes, it updates everywhere, not just in one isolated dashboard.

Key Responsibilities

Data Modeling & Architecture: Transition logic out of Metabase SQL queries and into modular, version-controlled models within AWS Redshift.
Build for History: Design and implement historical tracking (e.g., Slowly Changing Dimensions) so we can accurately report on "Active Cards" or user states at any point in the past.
Pipeline Optimization: Work alongside our data engineers to optimize AWS Airflow DAGs, ensuring data is clean, tested, and reliable before it hits the BI layer.
Stakeholder Partnership: Act as the primary point of contact for Finance, CX, and Marketing to translate business requirements into technical specs.
Process Governance: Implement a public Kanban/Agile workflow to manage intake, prioritize high-impact requests, and provide transparency to leadership.
Dashboard Consolidation: Audit our existing Metabase environment to move away from "disposable" dashboards toward a curated set of "Gold Standard" executive tools.

Technical Requirements

Expert SQL: You don't just write queries; you write performant, readable, and dry (Don't Repeat Yourself) code.
dbt (Data Build Tool): Deep experience using dbt to manage the transformation layer is highly preferred.
Data Warehousing: Strong understanding of AWS Redshift (dist keys, sort keys, and query optimization).
The "Modern Data Stack": Experience with version control (Git), orchestration (Airflow), and BI tools (Metabase, Tableau, or Looker).
Dimensional Modeling: A firm grasp of Kimball or Star Schema methodology to create tables that make simple reporting... simple.