Associate Data Analyst - Builders Program
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Key skills for this role
About the Role
Tamara's Builders Program is seeking a fresh graduate or early-career Data Analyst on an analytical engineering path. The role blends data analysis and data engineering to build trustworthy datasets, enable self-serve analytics, and make data AI-ready.
Key Skills for This Role
Responsibilities
- 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
- Answer ad hoc questions with clear analysis and explainable methodology
- Turn common questions into reusable, self serve assets: dashboards and AI enabled workflows
- Create AI friendly data products (well defined datasets, metrics, and documentation) that teams can query through AI tools
- Write basic tests, checks, and monitoring for key datasets and critical metrics
- Troubleshoot data issues and improve reliability from source to reporting
- 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 to accelerate SQL drafting, code scaffolding, and documentation, while validating outputs
Requirements
- Fresh graduate or < 1 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 and basic statistics
- 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
Full Job Posting
Why Tamara?
- We’re proud to be Saudi’s first FinTech unicorn.
- Our mission is to help people own their dreams by building the most customer centric financial super app in the world.
- Our teams are made up of innovators, problem solvers, and learners we thrive on curiosity and collaboration.
About The Program
- Our Flagship Builders Program is designed for ambitious graduates ready to step into real responsibility from day one.
- Designed for recent graduates and early career talent with up to two years of experience.
- You’ll be trusted with ownership, surrounded by high caliber peers, and mentored by leaders who expect excellence.
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, improve data quality, and enable self serve analytics.
Your Responsibilities
- Model and transform data for analysis: build and maintain clean, reusable datasets; contribute to a scalable metrics layer.
- Support analytics and decision making: answer ad hoc questions; turn common questions into reusable self serve assets; create AI friendly data products.
- Enable AI ready analytics: package datasets and metrics for reliable use by AI tools; write AI friendly documentation; partner with AI and platform teams.
- Ensure data quality and reliability: write basic tests, checks, and monitoring; troubleshoot data issues.
- Work effectively with engineering and product: collaborate on schema changes and pipeline improvements; translate ambiguous business questions into analyses.
- Use AI tools thoughtfully: accelerate SQL drafting and documentation; validate AI outputs and protect sensitive data.
Your expertise (must have)
- Fresh graduate or < 1 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 and basic statistics.
- 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).
- Experience creating AI ready data assets.
- Experience using AI assistants responsibly.
- 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.
- A key dataset or metric you built becomes usable through AI tools with consistent answers.
- You can spot when results look off, debug quickly, and explain the root cause.
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