Associate Data Analyst - Builders Program - (UAE National)

Overview

About The Program

About The Role

• We're looking for a fresh graduate or early-career Data Analyst on an analytical engineering path.
• This role blends the best of data analysis and data engineering.
• You will help turn raw data into
• trustworthy, well-modeled datasets
• that:
• Make definitions consistent (so "the number" means the same thing everywhere)
• Improve data quality and reliability
• Enable self-serve analytics and AI-assisted insights across teams (not just dashboards)
• You will also help make Tamara’s data

AI-ready

Your Responsibilities

• Model and transform data for analysis
• Build and maintain clean, reusable datasets (fact and dimension tables) that power reporting and self-serve analytics.
• Contribute to a scalable metrics layer: define, document, and align business definitions (for example, "active user", "approval rate", "default rate").
• Support analytics and decision making
• Answer ad-hoc questions with clear analysis and explainable methodology.
• Turn common questions into reusable, self-serve assets: dashboards and AI-enabled workflows (a curated dataset + definitions that an AI assistant can query correctly, plus validated example analyses).
• Create AI-friendly data products (well-defined datasets, metrics, and documentation) that teams can query through AI tools.
• Enable AI-ready analytics
• Package datasets and metrics so they can be reliably used by AI tools (clear grain, business definitions, data contracts, examples).
• Write AI-friendly documentation: glossary, metric definitions, common queries, and pitfalls.
• Partner with AI and platform teams to ensure critical tables are discoverable, permissioned correctly, and safe to use.
• Ensure data quality and reliability
• Write basic tests, checks, and monitoring for key datasets and critical metrics.
• Troubleshoot data issues and improve reliability from source to reporting.
• Work effectively with engineering and product
• Collaborate with data engineers on schema changes, event tracking, and pipeline improvements.
• Translate ambiguous business questions into measurable analyses, and communicate findings clearly.
• Use AI tools thoughtfully
• Use AI to accelerate SQL drafting, code scaffolding, and documentation.
• Validate AI outputs, document assumptions, and protect sensitive data.
• Your expertise (must have)
• Fresh graduate or \< 3 year of relevant experience (internships, projects, or part-time roles count).
• Solid SQL fundamentals (joins, aggregations, basic window functions).
• One programming language for analysis (preferably Python) with basic skills in:
• Data manipulation (tables/dataframes)
• Basic statistics (distributions, sampling intuition, confidence basics)
• Strong analytical thinking:
• Ability to define a problem, form hypotheses, validate data, and explain results.
• Strong attention to detail and commitment to accurate, reliable outputs.
• Ability to work effectively in a team-oriented environment.
• Nice to have
• Exposure to data modeling concepts (star schema, slowly changing dimensions, metrics definition).
• Familiarity with modern analytics stacks (dbt, BigQuery, Snowflake, Looker, PowerBI, Tableau) through coursework or projects.
• Experience creating AI-ready data assets (clean semantic layers, metric definitions, data contracts, documentation, and evaluation or sanity-check checklists) is a plus.
• Experience using AI assistants responsibly to accelerate analysis or analytics engineering work (for example, SQL drafting, code scaffolding, documentation).
• Experience with version control (Git) or basic software engineering practices.
• Understanding of event tracking and product analytics (funnels, cohorts, retention).
• Knowledge of responsible data handling (PII basics, access controls, safe sharing).

What Success Looks Like

• You can independently produce a well-structured analysis with clear assumptions and validation steps.
• You contribute at least one reliable dataset or transformation that becomes a shared building block for analytics.
• Stakeholders can answer more questions with self-serve and AI-assisted exploration, with less back-and-forth.
• A key dataset or metric you built becomes usable through AI tools with consistent answers (validated against a source-of-truth definition).
• You can spot when results look off, debug quickly, and explain the root cause.