Role Summary

You build and maintain data pipelines for our clients - different industries, different stacks, one engineering standard. Python and SQL will be your daily tools; a mentor and code review will be your safety net. We hire data engineers, not software developers who occasionally work with data: fundamentals, the habit of asking "why", and a real grasp of what the craft is about. The rest we teach.

The Mission

Build pipelines that produce data people can trust: they run on schedule, fail loudly, and are easy to fix - regardless of the platform.

The Tech Stack

• Core Languages: Python (clean, tested code), SQL (solid basics). Scala welcome.

• Processing Frameworks: PySpark (concepts to basics), Pandas and Polars for local analysis.

• Clouds (exposure to): AWS, GCP, or Azure - storage, compute, IAM.

• Orchestration: Airflow, Dagster, or similar (concepts).

• You will learn: dbt, Docker, CI/CD, Databricks/Snowflake, Terraform.

Your Skills

• Experience: 1+ year building software in Python.

• Python Engineering: clean modular code, OOP basics, and a project set up from scratch: virtual environment, dependencies, sensible layout, pytest tests for your own code.

• Git: daily fluency, not just familiarity: branches, commits, pull requests, review comments.

• SQL: joins, aggregations, window functions; any relational database (PostgreSQL, MySQL, SQL Server).

• DE Concepts: you chose data engineering deliberately. You can explain ETL vs ELT, what a warehouse is and how it differs from a database and a lake, and why pipelines must be idempotent.

• Processing & Orchestration: you know why Spark and Airflow exist (distributed processing, DAGs). Concepts are required; basic hands-on with at least one of them is a strong plus. Pandas or Polars for local work is assumed.

• Terminal: bash basics: navigate, grep logs, ssh.

• Cloud: conceptual understanding of one major platform.

• Language: English B2 or higher.

Your Responsibilities

• Build and maintain ETL/ELT pipelines from APIs, databases, and files.

• Validate your data before passing it downstream: row counts, nulls, duplicates.

• Write tests, participate in code reviews - your code and others'.

• Help troubleshoot and monitor pipelines in production.

• Learn deliberately - dbt, Snowflake, Terraform - and share what you learn.

Nice to Have

• Streaming: basic concepts (Kafka, Kinesis).

• NoSQL: exposure to MongoDB or DynamoDB.

• Formats: Parquet, Avro.

• Containers: Docker basics.

What we offer

• Long-term stability, competitive compensation, and a fast onboarding process.

• Conditions for steady career development.

• Development supported by dedicated mentors and a variety of programs focused on expertise and innovation.

• A well-equipped and cozy office supports comfort and productivity across all project stages.

• Welcoming atmosphere and a friendly corporate culture.

If you feel this opportunity resonates with you, apply now — we’re looking forward to getting to know you!