You will design and maintain reliable ETL pipelines, connect to external data sources, and help build scalable data foundations for product features, analytics, and AI-driven capabilities. This is a great opportunity to own meaningful data processes end-to-end and work closely with product, engineering, and data stakeholders.
Your Ownership Will Include
Design, build, and maintain end-to-end ETL pipelines for production use.
Work with large-scale cloud usage data from different sources and formats.
Build data flows using Apache Spark and Databricks or similar technologies.
Structure data using Data Lake / Lakehouse concepts, including Bronze, Silver, and Gold layers.
Integrate with external APIs and data sources.
Prepare and model data for product features, analytics, and AI-related capabilities.
Ensure data quality, reliability, and observability across pipelines.
Collaborate closely with backend engineers, data analysts, and product stakeholders.
3+ years of experience as a Data Engineer or in a similar data development role.
Proven experience building production ETL pipelines end-to-end.
Hands-on experience with Apache Spark, preferably PySpark or Scala.
Experience with Databricks or a similar data processing platform.
Strong understanding of Data Lake / Lakehouse architecture and data modeling.
Experience integrating with external APIs and multiple data sources.
Strong programming skills in Python, Scala, Java, or a similar language.
Understanding of data quality, monitoring, validation, and pipeline reliability.
Ability to work independently, take ownership, and collaborate effectively.
Good communication skills in English.
