Medior/Senior Data Engineer
Summary
Build and maintain scalable data pipelines and infrastructure using Python, Scala, Kafka, Airflow, and Kubernetes to power analytics and ML at a media-tech company.
In this role you will help shape the performance and architecture of our modern data stack, working with tools like AirFlow, 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.
About the role
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, ClickHouse, Docker, Bazel, Kubernetes, GCP, GitHub, CircleCI, Superset and many other, mostly open‑source, technologies.
Responsibilities
- Design, develop, deploy, scale and maintain ETLs, AirFlow 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.
Job requirements
- 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, ClickHouse, ElasticSearch, 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).
Our tech stack
- Python, Scala.
- AirFlow, Kafka, ClickHouse, GCP.
- Bazel, Docker, Kubernetes.
- Github, CircleCI, ArgoCD, Ansible, Superset, and many other, mostly open source, technologies.
- SQL, NoSQL and DBMS/OLAP.