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.
What You'll Do
- 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
- 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
Top Skills
What We Do
Television remains a vibrant cultural influence and an essential source of entertainment and information worldwide. Tremendous growth in content choices, and viewing platforms that allow us to watch anything, anytime, on any screen, has actually made it harder for viewers to discover and keep up with all the great programming available. It’s also more competitive for content providers to keep your attention, and for marketers to make strong, measurable connections with their target consumers. Technology that improves the viewing experience, enables content discovery, and addresses audience fragmentation across screens will strengthen television’s business model and relevance to consumers. Data is at the center of any solution to make TV better. Samba TV's technology is built into Smart TVs and easily maps to smart phones and tablets. By recognizing what's on screen, Samba TV learns what viewers like and using machine learning algorithms, enables discovery of shows and actors in a whole new way. Likewise, our data and measurement products are transforming the way stakeholders across the media landscape are thinking about their business. Given the dramatic growth in streaming services, connected devices, time-shifting, and multi-screen viewership, our data products solve real problems and create a meaningful competitive advantage for our clients.
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