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. & There is no playbook for that; our Tamarians are writing it. Our teams are made up of innovators, problem-solvers, and learners we thrive on curiosity and collaboration.
If this sounds like you: curious, driven, and ready to build, we’d love to meet you
Apply now and join the next generation of Builders!

About the Program: 

At Tamara, we believe exceptional talent deserves an exceptional launchpad.

Our Flagship Builders Program is designed for ambitious graduates ready to step into real responsibility from day one. This isn’t a rotational “observer” program, it’s a career accelerator built for those who want to build, own, and raise the bar early.
Designed for recent graduates and early-career talent with up to two years of experience, the program places you directly into high-impact roles across Product, Engineering, Design, and beyond. You’ll contribute immediately and grow at an accelerated pace.
From Product to Engineering, Design to Commercial, you’ll tackle meaningful challenges that shape how millions experience fintech across the region. You’ll be trusted with ownership, surrounded by high-caliber peers, and mentored by leaders who expect excellence.

Our January and June cohorts are your opportunity to move fast, think big, and start building what’s next - not someday, but now.

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 by building well-defined datasets, metrics, and documentation that can be safely used by AI tools (and people) across the company.

With the advancement of AI, we value people who have strong fundamentals and clear thinking. Understanding data structures, measurement, tradeoffs, and how to validate results matters more than memorizing tools. You'll learn how to use AI responsibly to move faster, while still owning correctness, data quality, and interpretation.

You will collaborate with a diverse ecosystem of engineers, product experts, and business to solve real problems that impact our customers and business outcomes.

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 < 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 (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.

Artefact is a new generation of data service providers, specialising in data consulting and data-driven digital marketing. It is dedicated to transforming data into business impact across the entire value chain of organisations. We are proud to say we’re enjoying skyrocketing growth.The backbone of our consulting missions, today our Data consulting team has more than 400 consultants covering all Artefact's offers (and more): data marketing, data governance, strategy consulting, product owner…

About the role ...

As a Cloud Architect Manager at Artefact, you will be responsible for designing, implementing, and overseeing robust, scalable, and secure cloud solutions that support our clients’ data and technology ecosystems. You will collaborate closely with technical teams and business stakeholders to ensure cloud architecture is aligned with business needs, optimizing tool selection, cost efficiency, and scalability. Additionally, you will lead technical teams, mentor engineers, and contribute to Artefact’s growth by driving innovation and promoting best practices in cloud technology.

What you'll be doing ...

• Lead the design and implementation of cloud infrastructure architectures (cloud, on-premise, hybrid) aligned with business requirements.
• Define and enforce best practices for cloud platform deployment and configuration, security, networking, operations and tools selection.
• Oversee the integration of cloud platforms with data pipelines, storage, and compute services.
• Standardize coding, deployment, and monitoring practices across cloud environments.
• Design and implement automated solutions to set up, monitor, and adjust cloud infrastructure efficiently.
• Lead problem-solving initiatives for complex technical challenges related to cloud environments.
• Provide technical leadership across multiple cloud projects, ensuring efficient resource management.
• Foster teamwork by mentoring, coaching and guiding cloud engineers and ensuring knowledge transfer.
• Manage client relationships regarding project expectations, timelines, and outcomes.
• Contribute to Artefact's growth by maintaining tech partnerships, leading technical proposals to support project planning and identify potential growth areas with existing accounts.

What you'll need ...

• Proven track record in architecting and deploying large-scale Google Cloud solutions
• Deep expertise in GCP security, networking, and compliance best practices (VPC Service Controls, IAM, Organization Policy)
• Designed end-to-end GCP environments that are highly available, secure, and cost-efficient—leveraging Google Kubernetes Engine (GKE), Cloud Run, Compute Engine, and Cloud SQL
• Defined global DR/HA strategies using Cloud Load Balancing, Cloud CDN, and automated failover with multi-region Bigtable/Spanner or persistent disk replication, achieving sub-hour RTOs for critical workloads
• Architected end-to-end data pipelines—built both streaming and batch ETL workflows using Pub/Sub, Dataflow (Apache Beam), and Dataproc to ingest high-throughput events into BigQuery for real-time insights.
• Designed scalable data models in BigQuery—implemented dimensional schemas with partitioning and clustering to achieve sub-second query times on petabyte-scale datasets.
• Orchestrated and scheduled workflows leveraging Cloud Composer (Airflow) and Workflows—added automated retries, data-quality validations, and alerting to guarantee pipeline reliability.
• Automated infrastructure lifecycle via Terraform and Cloud Build pipelines—implementing auto-scaling, patch management, and monitoring workflows that boosted operational efficiency
• Mastery of cost control in GCP: committed-use discounts, custom machine types, billing export analytics, and budget alerts, plus high-availability patterns for resilience
• Partnered with data teams to fine-tune architectures on Cloud Storage, BigQuery, Dataflow, and Dataproc—delivering performant, scalable analytics platforms
• Proven leadership in guiding cross-functional teams and steering complex, multi-phase cloud projects to on-time delivery
• Exceptional communicator, adept at crafting business-focused presentations and leading stakeholder workshops on GCP strategy
• Strong problem-solver, experienced in diagnosing and resolving architectural challenges across compute, data, and networking domains
• Skilled at stakeholder management, from technical deep dives with engineering teams to strategic discussions with C-level executives
• Mentorship & coaching: developing cloud engineering talent through hands-on training, code reviews, and best-practice evangelism
• Business growth driver, recognized for leveraging GCP innovations to unlock new revenue streams and streamline operations