At least 3 years of experience in data engineering or platform data engineering roles.
Documented experience working with large datasets and high-volume event data.
Strong Python engineering skills.
Hands-on experience with event ingestion and streaming, preferably using Kafka.
Experience with data lake or lakehouse architectures.
Good SQL skills and the ability to work with analytical data models.
Experience with Spark for large-scale data processing.
Experience with Kubernetes and Helm.
Practical understanding of observability, monitoring and data pipeline reliability.
Ability to work in environments that are still being built, improved and standardized.
A proactive working style, with the ability to identify what needs to be done rather than waiting for fully defined tasks.
Good collaboration skills in a mixed team setup, including engineering, DevOps, product and data science.

We’re hiring a **Data Engineer** to work on a multi-tenant Customer Data Platform used by mobile network operators across several countries.
This is a hands-on platform data engineering role, focused on high-volume event ingestion, streaming, lakehouse architecture, distributed processing, observability and deployment support across several environments.
The platform is already live, with several deployments in place, but it is not yet a fully mature setup. The current focus is on scalability, pipeline improvements, operational stability, CI/CD maturity and adapting the standard architecture to the needs of each deployment.
As part of the Data Platform team, you will work closely with engineering, DevOps, data science and product colleagues. The team setup includes backend, frontend, mobile, DevOps, data engineering, data science, product management and external vendor contributors.
Your role will be to help build and improve the data platform behind large-scale telecom event processing. A major part of the role will be focused on building and improving pipelines, data models and platform capabilities.
Build and maintain event ingestion and streaming pipelines using **Kafka**.
Work on topic structures, partitioning strategies, retention policies and consumer logic for **multi-tenant** environments.
Develop **Python**-based ingestion services and data loaders.
Work with **lakehouse architecture** based on Iceberg and Nessie.
Design, optimize and maintain Iceberg tables, including partitioning, compaction, retention and metadata management.
Build and improve **Spark** jobs for cleaning, normalizing, enriching and processing high-volume event data.
Help improve data quality, **observability** and monitoring across ingestion, processing and storage layers.
Support Kubernetes and Helm-based deployments together with DevOps colleagues.
Handle sensitive and **PII-related data** with strong attention to tenant isolation, encryption and governed data flows.
Collaborate with product, data science and engineering teams to translate business and technical needs into scalable data structures.
A hands-on platform engineering role with direct impact on a live international data product.
Work with modern data platform technologies: Kafka, Python, Spark, Iceberg, Nessie, Kubernetes and SQL-based modelling.
Exposure to high-volume telecom data and multi-tenant platform architecture.
Flexible collaboration: Employee or Contractor
Hybrid work setup in Bucharest or relatively remote from Romania (outside Bucharest) - a couple days per month spent in Bucharest office.
Variable bonus component paid quarterly, based on company, team and individual objectives.
We look forward to your application or a referral.
***Note:*** *Only qualified candidates will be contacted for further steps.*
Remote Romania / Hybrid Bucharest