Младший разработчик Big Data (Python)

About the role

About the company

Context

Responsibilities

• Develop and maintain analytical data models
• Work with SQL-based transformations and data pipelines
• Contribute to data quality checks and validation logic
• Help build and maintain data products used by business and engineering teams
• Work with orchestration tools and scheduled data workflows
• Take part in discussions about data modelling, metrics and definitions
• Investigate data issues and help identify root causes
• Improve the reliability and cost efficiency of data processing

Qualifications

Required

• Strong fundamentals in SQL
• Good working knowledge of Python for data processing
• Understanding of relational data models and basic data modelling concepts
• Understanding of how analytical data is used for reporting and decision-making
• Ability to read, understand and modify existing data pipelines or models
• Experience with Git and standard development workflows
• Structured thinking and attention to data correctness
• Open to constructive feedback
• English sufficient for documentation and team communication

Strong plus

• Familiarity with analytical data modelling approaches (star schema, slowly changing dimensions)
• Experience with data transformation tools (dbt or similar)
• Experience with workflow orchestration tools (Airflow or similar)
• Familiarity with cloud data warehouses (BigQuery or similar)
• Exposure to BI tools or data visualisation
• Interest in data quality, metrics and data platform design

What we do not expect

• Expert-level SQL or Python
• Prior professional data engineering or analytics engineering experience
• Mastery of all listed tools (dbt, Airflow, BigQuery, BI platforms)
• Deep knowledge of data warehouse internals or distributed data processing

Eligibility

• Saudi nationals only
• We welcome both current students and fresh graduates
• We expect a full-time level of engagement. The programme is not part-time. Students can align time for classes or exams with their mentor in advance, but performance, ownership and involvement are expected at a full-time level

Benefits

• Six months, starting autumn 2026
• Paid internship, funded by Tabby
• Full integration into an engineering team
• Distributed engineering team across multiple countries
• Saudi passport required.
• A path to a junior data engineer or analytics engineer role afterwards. That is our intent and what we aim for, not a guarantee: it depends on how the internship goes

About TaxDome

How we work

About this role

Our tech stack

• Cloud: Google Cloud Platform (BigQuery, GCS, GKE/Kubernetes)
• DWH: BigQuery (cross-region, column-level security, PII masking)
• Transformations & data load: dbt & Python
• Orchestration: Apache Airflow on Kubernetes, Python ETL
• BI: Data Studio
• Process: GitLab, CI checks for layer architecture, AI-assisted code review
• AI: Claude Code, LLM-driven development and analytics

What you'll be responsible for

• Onboard new data sources end to end — from raw ingestion to certified marts.
• Advance data quality: tests, contracts, metrics dashboards, and anomaly monitoring.
• Contribute to warehouse architecture evolution: conventions, code review, documentation, and AI-ready data contracts.
• Build and maintain dashboards for product and business teams; design metrics and bring them to self-service.
• Drive analytical work from business question to reproducible analysis with a clear decision recommendation.
• Develop models and analyses with AI assistants (Claude Code and similar) as a primary workflow, not a shortcut.
• Help build AI-native team processes: automating routine work, agentic pipelines for documentation, testing, and ad-hoc analysis.
• Act as the translator between business questions and data model design.

What you bring

Must-have

• Proven depth in data roles — analytics engineering, data analysis with an engineering orientation, or data engineering with strong analytical capability.
• Advanced SQL — you write and review complex queries comfortably and reason about performance trade-offs.
• Hands-on production experience with dbt: incremental models, snapshots, tests, macros, and project organisation.
• Experience designing DWH layers and reasoning about trade-offs: normalisation vs. marts, full rebuild vs. incremental, SCD2.
• Python for ETL tasks and data analysis.
• Experience building dashboards and metrics with the ability to drive analysis through to a decision — not just a table.
• Real, embedded experience working with AI assistants in your daily workflow — LLMs as a core tool, not an occasional experiment.
• Experience with Airflow or another orchestration platform
• Advanced stakeholder and project management: able to take a vague business problem, decompose it into a plan, and drive it to completion independently.

Nice-to-have

• SaaS product analytics background: PLG metrics, funnel analysis, cohort modelling.
• Experience with GA4, HubSpot, or Stripe data.
• Understanding of data privacy requirements: GDPR, PII masking, policy tags.
• Experience building agentic pipelines or automations on top of LLM APIs.

What we offer

• Competitive compensation, paid in USD, transparently shared before the first interview
• Performance-based incentive plan
• Fully remote work with flexible hours
• 30 paid days off annually, plus sick days as needed
• Health & well-being support
• Learning & development budget to support your professional growth
• English lessons reimbursement
• Co-working space reimbursement
• Company-provided equipment (conditions may vary depending on the role)
• A high level of autonomy and ownership in your work
• The opportunity to make a real impact in a fast-growing global SaaS company
• A collaborative, international team with a strong product mindset

AI Platform is one of our most forward-looking teams. It owns the shared infrastructure and platform layer that AI work across the company runs on — everything from how LLMs are used and measured to the knowledge and retrieval systems built on top of them.

 

A large share of the work is data engineering, because there's a lot of information to move, process and make sense of: LLM usage and cost analytics (tokens, cache, spend), and applied systems like contextual knowledge bases and vector search.

 

Playrix works with roughly 7 petabytes of data.

• PySpark, Databricks, dbt, Redshift, RDS, PostgreSQL


• AI: Claude, KimiK3, and Codex with configured repository rules, a growing catalog of specialized skills, and agentic modes for specific tasks. MCP integrations with internal infrastructure (GitHub, Asana, Amplitude, etc.). Agentic workflows cover the entire cycle—from generating game elements and automated tests to automatically creating and reviewing PRs

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  The opportunity for continuous development in a team of 500+ professional engineers: we have a huge knowledge base and a mentoring system that allows you to adapt quickly.
  

  
  


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  Ability to move between areas and not only within development (Project Management, marketing, etc.).
  

  
  


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  Tasks that require you to make safe and effective architectural decisions as well as opportunities to apply interesting programming approaches.
  

  
  


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  An emphasis on developing each specialist's product ideas Time to play the games that you're developing, so you can envision how this or that feature will work for the user.
  

  
  


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  The ability to switch out processes and approaches for more efficient ones without lengthy approvals or bureaucracy.