Senior Data Engineer

About the role

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.

Responsibilities

Building and owning data pipelines and the platform layer that AI products across the company depend on

Analytics on LLM usage — cost, token and cache economics, financial reporting on AI spend

RAG systems: contextual knowledge bases, vector and semantic search infrastructure

Making the platform operable — profiling, monitoring, observability of the pipelines you own

Building interfaces and services on top of the data, not just landing it (auth, APIs, interactive access for consumers)

Requirements

Strong commercial data engineering experience with a modern stack — e.g. Databricks, dbt, Airflow, Dagster, Greenplum, Snowflake, or similar

Confident Python, plus a working backend mindset: how authorisation works, how a service is exposed and consumed. We're looking for a data engineer who is comfortable building something interactive, not only delivering datasets downstream

Practical understanding of how to profile and monitor data systems in production

Real understanding of the AI/LLM layer — vector databases, embeddings, contextual and semantic search, RAG architectures, how autonomous agents work. This is an AI Platform team: we expect conceptual grasp of these technologies, not just having used a coding assistant

Cloud experience (AWS ideally)

Our stack

• 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

We offer

• 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.

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

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

• 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.

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