Samba is a media intelligence company. We know what the world is watching, reading, and thinking about — in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it’s the most interesting data asset on the planet, because it’s the most culturally relevant.
We are seeking a skilled Data Engineer to join our Data Technology team in Warsaw. Our team builds and maintains the data platform that powers the entire organization — from ingestion and analytics to reporting, from viewership and contextual datasets to scalable applications that enable data-driven decision making. You will contribute to the design, development, and maintenance of our data infrastructure, working primarily on AWS, Databricks, BigQuery, and Snowflake technology.
At this level, you are a self-sufficient contributor who takes clear ownership of well-scoped pipeline components and features, collaborates effectively with teammates and cross-functional stakeholders, and begins developing the breadth to navigate the full data lifecycle. You bring 2–4 years of hands-on experience, write production-quality code, and are ready to grow toward greater technical autonomy.
Design, build, and maintain scalable data pipelines supporting both internal and external data consumers, using Apache Spark (PySpark), Airflow, Databricks, and BigQuery/Snowflake
Develop and optimize data transformations for large-scale datasets, applying modern table formats such as Delta Lake and Iceberg
Own and operate Airflow DAGs and orchestration workflows, ensuring reliable and timely delivery of data products
Participate in the modernization of data frameworks and integrations across Databricks and BigQuery environments
Build and integrate data validation and quality assurance tooling using frameworks such as Great Expectations or similarImplement monitoring, logging, and alerting for data workflows to ensure production reliability
Debug and resolve pipeline issues across distributed environments, including cloud storage (AWS S3/GCS), databases, and orchestration tools
Contribute to the implementation of data governance and access controls using Databricks Unity Catalog
Collaborate with data scientists, analysts, and software engineers to deliver governed, reusable data assets
Participate in code reviews, contribute to documentation, and help raise engineering standards within the team
Identify bottlenecks in the development lifecycle and contribute ideas to improve them
Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience
2–4 years of professional experience in data engineering or a closely related role
Strong proficiency in Python and SQL; hands-on experience building and debugging data pipelines and automation scripts
Experience with Apache Airflow for workflow orchestration, including DAG development, operator configuration, and troubleshooting
Hands-on experience with Databricks and Apache Spark (PySpark) for large-scale data processing
Familiarity with modern table formats such as Delta Lake or Iceberg
Experience with cloud infrastructure (AWS and/or GCP), including S3/GCS bucket management and cloud data services
Experience with database migration workflows and version-controlled configuration management (Git)
Strong debugging and problem-solving skills with the ability to trace issues across distributed systems
Ability to manage a queue of operational tickets and prioritize based on SLA urgency
Strong communication skills; comfortable working asynchronously in a distributed, cross-timezone team
Nice to Have
Experience with BigQuery or Snowflake as primary data warehousing platforms
Familiarity with Databricks Unity Catalog for data governance and metadata management
Experience with Kubernetes (EKS, GKE) or containerized data workflows
Knowledge of CI/CD pipelines for data platform environments
Experience working with REST APIs for data access, automation, or integrationFamiliarity with FinOps concepts or cloud cost management principles
Background in ad tech, media measurement, or streaming data domain
