Medior​/Senior Data Engineer

In this role you will help shape the performance and architecture of our modern data stack, working with tools like Air Flow, Kafka, Python and Kubernetes to build scalable, high-impact data solutions in close collaboration with our engineering (backend, machine learning, etc) and non-engineering (such as marketing, finance, legal, etc) teams.

We are looking for a passionate Senior/Medior Data Engineer to join our team. You will be responsible for the overall performance and architecture of our Data Stack, as well as software and service development within the data domain. Our stack includes Python 3.14, Scala, Kafka, Airflow, Click House, Docker, Bazel, Kubernetes, GCP, Git Hub, Circle

CI, Superset and many other, mostly open‑source, technologies.

• Design, develop, deploy, scale and maintain ETLs, Air Flow data pipelines and data services in production.
• Resolve problems, with end‑to‑end ownership of data quality in our core datasets and data pipelines.
• Design data models, tables, data structures, improve on data storage architecture and queries performance across various business domains within the company.
• Assist colleagues across technical challenges.
• Review, maintain, refactor and extend distributed systems in production. Support other teams for usage and integration with those systems.
• Maintain the technical excellence of the data and software engineering practice.
• Work with the Product Manager and other stakeholders, taking part in forming, prioritizing and executing data engineering backlog.

• 3+ years of professional experience as a Data Engineer, Software Engineer, or similar role working with large‑scale data systems and infrastructure.
• Bachelor’s degree in Software Engineering, Computer Science, or relevant field, or equivalent practical experience.
• Proficiency in Python and/or Scala with strong software engineering skills.
• Experience designing, building, and optimizing large‑scale data pipelines in distributed environments using tools such as Kafka, Click House, Elastic Search, Cassandra, Spark, etc.
• Deep understanding of data architecture principles, including replication, sharding, consistency, scaling (horizontal and vertical), quorum, and idempotency.
• Proven ability to improve pipeline performance, cost‑efficiency, and usability.
• Experience mentoring team members and leading technical projects is a plus.
• Basic knowledge of analytics and machine learning concepts is a plus.
• Excellent analytical, communication skills, and fluent in English (both spoken and written).

• Python, Scala.
• Air Flow, Kafka, Click House, GCP.
• Bazel, Docker, Kubernetes.
• Github, Circle
  
  CI, ArgoCD, Ansible, Superset, and many other, mostly open source, technologies.
• SQL, No
  
  SQL and DBMS/OLAP.