Data Engineer I – Cloud & Scalable Data Platforms

About the role

We are looking for an experienced Data Engineer to design, develop, and support scalable data platforms and pipelines. The role works closely with product, analytics, and platform teams to deliver reliable, cloud‑native data solutions across multiple markets.

Key responsibilities

• Develop and optimise batch and streaming data pipelines using Python, Spark/PySpark and SQL.
• Build and maintain cloud‑native data platforms such as data lakes, warehouses and real‑time processing systems.
• Take end‑to‑end ownership of data initiatives, from solution design and testing to deployment, performance tuning and production support.
• Collaborate with product managers, analytics, and engineering teams to translate business requirements into high‑quality data solutions.
• Monitor platform health, troubleshoot production issues and continuously improve reliability.

Required profile

• At least 1 year of experience in data engineering or big‑data environments.
• Strong experience designing scalable ETL/ELT pipelines with Python, Spark/PySpark and SQL.
• Hands‑on experience with modern data‑lakehouse technologies (e.g., Apache Iceberg, Hive, Trino, BigQuery, Redshift, ClickHouse, Snowflake).
• Familiarity with cloud object storage such as Amazon S3 or compatible platforms.
• Excellent analytical, problem‑solving and communication skills.

Required skills

• Python
• Spark / PySpark
• SQL
• Apache Iceberg, Hive, Trino, BigQuery, Redshift, ClickHouse, Snowflake
• Amazon S3 (or compatible object storage)
• Kubernetes
• Docker
• GitLab CI/CD
• Apache Kafka (good to have)
• MySQL, Oracle (RDBMS)
• MongoDB (optional)
• SQLAlchemy, PyMongo
• LangGraph, MLOps (LLM‑based applications)