Data Engineer

Warsaw,WarsawInternational Full Time Employeeentry

Data EngineeringData EngineerDataData & AI

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airflowawsgcppostgresqlpythonsparksqldistributed-systemsetl

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 Data Engineer to join our Technology Operations team. This team drives the operational delivery of Samba measurement and data licensing products—the core revenue engines of the business. As a Data Engineer, you will design, build, and maintain the data pipelines, automation workflows, and infrastructure that ensure reliable, on-time delivery of customized data products to clients. In addition, you will be building and maintaining agentic AI-driven workflows to automate repetitive operational tasks, enhance data validation, and streamline end-to-end delivery processes. You will work hands-on with production systems, debug complex delivery issues across distributed environments, and collaborate cross-functionally to continuously improve operational efficiency through both traditional engineering and intelligent automation.

Design, develop, and maintain data pipelines for the end-to-end delivery of measurement reports and data licensing products to clients, using Apache Airflow, Databricks, and PySpark

Configure and troubleshoot push delivery workflows, including database migrations, DAG configuration, GCS/S3 bucket management, and client-facing file delivery verification

Build and operate agentic automation workflows to reduce manual operational toil, improve data validation, and accelerate delivery turnaround times

Investigate and resolve production data issues by navigating complex systems spanning Airflow DAGs, PostgreSQL databases, cloud storage (AWS/GCP), and client-specific delivery configurations

Manage and execute custom product delivery requests, including measurement requests, data licensing operations, matching file generation, cross-reference file creation, and client delivery setup

Develop data validation and quality assurance tooling to ensure accuracy and consistency of custom datasets before they reach clients

Write and maintain database migrations to update delivery configurations, report integrations, and client setup across staging and production environments

Collaborate cross-functionally with product, measurement sciences, client services, and engineering teams to translate delivery requirements into reliable, automated solutions

Document operational processes, runbooks, and delivery workflows to enable knowledge sharing and team scalability

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience

3–5+ years of professional experience in data engineering, software engineering, or a related operational engineering role

Strong proficiency in Python, with 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 failed runs

Proficiency in SQL for data extraction, transformation, and database administration, including complex queries with joins, window functions, and JSONB manipulation

Experience with cloud infrastructure (AWS and/or GCP), including S3/GCS bucket management, IAM role assumption, and ephemeral credential workflows

Familiarity with Databricks and PySpark for large-scale data processing and transformationExperience 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 (databases, orchestration tools, cloud storage, delivery endpoints)

Ability to work independently, manage a queue of operational tickets, and prioritize based on SLA urgency