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.

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 Tamara's Builders 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 Associate Data Scientist on a builder path.

This role blends product thinking, applied statistics, and production-minded analytics. You will:

• turn ambiguous problems into measurable questions
• build reliable measurement (metrics and experimentation)
• create lightweight models and decision tools that teams can use in real workflows
• partner closely with engineering, product, and risk to improve customer outcomes and business performance
• build AI-assisted decision workflows (for example, LLM-powered analysis copilots backed by a curated metrics layer, with clear guardrails and validation)

With the advancement of AI, we value people who have strong fundamentals and clear thinking. Understanding data generation, 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, robustness, and interpretation.

Your responsibilities

• Define problems and measurement
  • Translate product or business questions into testable hypotheses and clear success metrics.
  • Build and maintain metric definitions and analysis templates so decisions are consistent and repeatable.
• Experimentation and causal thinking
  • Design, analyze, and interpret A/B tests (and quasi-experiments when randomization is not possible).
  • Partner with product and engineering to ensure correct tracking, guardrails, and experiment quality.
• Modeling for decisions (practical ML)
  • Build baseline predictive and segmentation models (for example, churn propensity, risk signals, customer clustering) with clear evaluation and limitations.
  • Focus on models that are actionable: who will use them, when, and what decision they change.
• Applied AI (LLMs) for analytics and decisioning
  • Prototype small, safe LLM use cases that improve how teams explore data (for example, natural-language Q&A over a curated dataset, summarizing experiment results, or generating investigation checklists).
  • Help define evaluation and validation approaches for AI-assisted outputs (sanity checks, golden queries, offline test sets, and human review loops).
  • Partner with platform and security stakeholders to ensure AI workflows are permissioned correctly and avoid leaking sensitive data.
• Analytics that ships
  • Create reusable, well-documented analysis assets: datasets, feature tables, notebooks, dashboards, and simple services or jobs.
  • Collaborate with data engineering to productionize reliable pipelines where needed.
• Data quality and reliability
  • Validate data inputs, monitor key metrics, and investigate anomalies.
  • Document assumptions and build sanity checks so analyses and models are trustworthy.
• Responsible use of AI tools
  • Use AI to accelerate coding, documentation, and exploration.
  • Validate AI outputs, protect sensitive data, and follow safe data handling practices.

Your expertise (must have)

• Fresh graduate or < 1 year of relevant experience (internships, capstone projects, or part-time roles count).
• Strong SQL fundamentals (joins, aggregations, window functions).
• One programming language for data (preferably Python) with basic skills in:
  • Data manipulation (tables/dataframes)
  • Statistics fundamentals (distributions, sampling intuition, confidence basics)
  • Basic modeling (regression/classification basics and evaluation intuition)
• Strong analytical thinking:
  • Ability to define a problem, validate data, and explain results clearly.
• Strong attention to detail and commitment to accurate, reliable outputs.
• Ability to work effectively in a team-oriented environment.

Nice to have

• Exposure to experimentation platforms or frameworks (for example Statsig, Optimizely, internal experimentation tooling).
• Familiarity with modern analytics stacks (dbt, BigQuery, Snowflake, Looker, PowerBI, Tableau) through coursework or projects.
• Exposure to ML tooling (scikit-learn, notebooks, basic MLOps concepts) and version control (Git).
• Familiarity with LLM concepts (prompting basics, retrieval/RAG intuition, and evaluation approaches) through coursework or side projects.
• Understanding of product analytics concepts (funnels, cohorts, retention) and causal pitfalls (selection bias, confounding).
• Experience creating AI-ready data assets (clean semantic layers, metric definitions, data contracts, documentation, and sanity-check checklists).
• Knowledge of responsible data handling (PII basics, access controls, safe sharing).

What success looks like

• You can independently deliver an end-to-end analysis with clear assumptions, validation steps, and a concrete recommendation.
• You help ship at least one decision tool (experiment, metric framework, or lightweight model) that changes a product or business decision.
• Stakeholders can run repeatable analyses with less back-and-forth, and your work reduces ambiguity in key metrics.
• You can spot when results look off, debug quickly, and explain the root cause.

The Role:

Ziina is looking for a Senior Data Platform Engineer to join our team. This role is an exciting opportunity to build the foundations of our data platform from the ground up — the pipelines, warehouse, and tooling that will power decision-making, analytics, and future ML/AI capabilities across the company. We're at a major inflection point in our growth, and we're looking for the right person to shape how data is ingested, modeled, and consumed at Ziina as we scale.

As part of a small, agile team, our ideal candidate is a creative problem-solver with the vision to build from the ground up and the skill to own systems end-to-end. You'll thrive in our fast-paced environment by making sound tradeoffs and focusing on high-impact solutions. We're looking for someone who is as ambitious as we are to deliver high-quality products to our users. In short, we want owners who are ready to build big things with us.

As a Senior Data Platform Engineer at Ziina you will:

• Build and maintain scalable data pipelines and processing systems that ingest data from transactional databases, object storage, and external APIs
• Design and evolve our data warehouse architecture and data models to support analytics, reporting, and ML/AI use cases
• Improve the reliability, observability, and performance of our data systems
• Implement data quality checks and monitoring to ensure trusted, accurate data across the company
• Enable self-service data access and analytics so product, engineering, and business teams can make data-driven decisions independently
• Support the foundational capabilities that will power our ML and AI use cases as they grow

To succeed in this role, you likely:

• Bring 5+ years of experience in Data Engineering, Data Platform, or Analytics Engineering
• Have strong expertise building and maintaining data pipelines with orchestration tools like Dagster, Airflow, or Prefect, and writing ETL/ELT workflows in Python
• Are proficient with modern data warehouses (Snowflake, BigQuery, or Redshift) and transformation frameworks like dbt
• Have hands-on experience ingesting data from a mix of sources — PostgreSQL, S3, external APIs — supporting both transactional and event-driven use cases
• Are familiar with data quality, observability, and monitoring practices for production data systems
• Use the latest AI tools and technologies to boost your productivity
• Are based in, or open to relocating to, the UAE

What would amaze us

• Proven experience building data platforms at fintech or other high-scale, high-reliability companies
• A track record of designing data warehouse architectures and models that scaled cleanly as the company grew
• History of building self-service analytics tooling that meaningfully expanded who in the company could work with data
• Experience enabling ML/AI workloads on top of a data platform — feature stores, ML pipelines, or serving infrastructure
• Active engagement in the data community through open source contributions, conference speaking, or technical writing

Our tech stack:

The data platform is a core investment area at Ziina. Our current stack is:

• Snowflake as our data warehouse, with dbt for data transformation and modeling
• Dagster for orchestrating data pipelines, with Python for building ETL/ELT workflows
• Data ingestion from PostgreSQL, S3, and external APIs, supporting both transactional and event-driven use cases
• Metabase for analytics and internal reporting

The broader Ziina engineering stack that the data platform integrates with:

• Typescript, Node.js and Nest.js for our main application's backend
• GraphQL Federation for our client-facing APIs and Kafka for our inter-service communication
• PostgresSQL for consistent and durable storage, Redis for quick fetching, Elasticsearch for quick searching
• AWS for hosting our cloud infrastructure and Kubernetes for orchestrating our workloads
• Terraform for IaC, GitHub Actions for CI/CD

About the team

The Careem Data Science team’s mission is to drive competitive value from data at scale through building AI models to optimize user experiences, decision-making, operational efficiencies, and lead the region’s AI ecosystem. As one of the tech leaders in this team, you will be at the forefront of fulfilling this mission. You will be working with the top data science talent of the region, while innovating on our user experience using GenAI.

What you'll do

• Be part of a 0-1 AI transformation for the Careem app from a personalization perspective.
• Build a long-term vision on how we can rethink our customer acquisition and engagement strategies, leveraging data in our decision-making.
• Drive exploratory analysis to understand the ecosystem, user behavior, and identify new levers to help move metrics and build models of user behaviors for analysis and product enhancements.
• Shape and influence data/ML models and instrumentation to optimize the product experience and generate insights on new areas of opportunity and new products.
• Provide product leadership by sharing data-based recommendations to communicate the state of the business, the root cause of change in metrics, and experimentation results, influencing product and business decisions
• Implement scalable machine learning algorithms that will be used in production on big data.
• Embark on exploratory data analysis projects to achieve a better understanding of phenomena as well as to discover untapped areas of growth and optimization. 
• Answer complex analytic questions from big data sets to help Careem shape its products and services in a better way.
• Help define and track the appropriate key metrics for specific projects. 
• Design and run randomized controlled experiments, analyze the resulting data, and communicate results with other teams.
• You will always challenge the status quo and continually investigate new data processing technologies and seek to ensure that we follow the industry best practices.
• Build and deploy retrieval augmented generation systems and other applications of large language models.

What you'll need

• 7-9 years of experience in data mining, predictive modeling, time series analysis, machine learning, Big Data methodologies, and transformation and cleaning of both structured and unstructured data.
• Advanced degree in a quantitative discipline such as Physics, Statistics, Mathematics, Engineering or Computer Science.
• Solid experience with Deep Learning Techniques (e.g, attention, retrieval models, etc)
• 1-2 years of industrial experience in personalization, recommendation or search is a MUST. Preferably working in a product-driven company.
• Strong problem-solving and coding skills.
• Solid knowledge of AB testing, classic ML, and DL
• Solid understanding of recommendations, ranking, and retrieval. 
• Proficiency and demonstrated experience in: Python, SQL, Spark, Hive.
• Demonstrated experience with database technologies (e.g. , Hadoop, BigQuery, Amazon EMR, Hive, Oracle, SAP, DB2, Teradata, MS SQL Server, MySQL).
• Demonstrated experience with business intelligence and visualization tools (Tableau, MicroStrategy, ChartIO, Qlik) along with geospatial data processing skill, is a plus.