Engineer, Data Engineering

Appodeal is a dynamic US-based product company with a truly global presence. We have offices in Warsaw, Barcelona and Parkland (FL), along with remote team members located around the world. Our company thrives on diversity, collaboration, and innovation, making us a leader in the mobile app monetization space. At Appodeal, we're more than just a company-we're a team united by a common mission: We are seeking a skilled Data Engineer to join our Mobile Growth Platform UA team in Barcelona. The ideal candidate will be responsible for building, maintaining, and optimizing our data infrastructure, ensuring seamless data flow and accessibility for ML and DA, analytical and business needs. Design and build scalable data pipelines: Architect, construct, and maintain robust ETL/ELT pipelines using the Databricks platform (Apache Spark, Delta Lake, Delta Live Tables) for batch and streaming data ingestion from various sources. Manage data architecture: Define and maintain a scalable and secure data platform architecture that integrates Databricks (for data lakehouse management and complex transformations) with specialized OLAP engines (ClickHouse/Druid for fast querying). Design, develop, test, and orchestrate data workflows to streamline pipelines. Optimize performance and reliability: Monitor, troubleshoot, and fine-tune data workflows and database performance (e.g., Spark job optimization, ClickHouse schema tuning, Druid data partitioning) to ensure maximum efficiency and reliability. Contribute to the development of internal tools for automating data processes and business workflows, enhancing efficiency and scalability. Collaborate closely with product, MLOps and data science teams to ensure an optimal data experience for all ML practitioners and data consumers. 3-5+ years of professional work experience in data engineering. ~ Strong expertise in Python and common data frameworks (Pandas, PySpark). ~ Experience with modern data storage solutions: Data lakes (Delta tables), and data warehouses. ~ Experience with data orchestration tools (Dagster, Airflow). ~ Experience with streaming technologies (Kafka, Spark structured streaming). ~ Experience with cloud platforms (AWS S3, EC2 etc.). ~ OLAP Databases: Hands-on experience with ClickHouse and/or Apache Druid for real-time analytics and low-latency data serving. ~ Strong problem-solving skills and ability to excel in a fast-paced startup environment. ~ Experience in building ML data platforms.