Data Developer - Temporary

Data Developer Job Description (temporary position)

Browsi is looking for a Data Developer to join our growing Data team. We're looking for someone who enjoys taking ownership, learns quickly, and is excited to work on production data systems from day one.

Browsi builds data-intensive AI products for the digital advertising ecosystem.

Every day we ingest, process, model, and serve billions of advertising events that power both our publisher optimization platform and our advertising intelligence products for brands and agencies.

Our Data team is at the heart of both products, building the data platform that powers customer-facing data products and enables data-driven decision making across the company.

Location & Work Model

This is a full-time temporary position (maternity leave replacement) based in Tel Aviv, Israel, operating under a hybrid work model that combines office and remote work.

Responsibilities

• Design, develop, optimize, and maintain scalable data pipelines and ETL workflows.
• Build scalable datasets that power analytics, AI models, and customer-facing products.
• Develop reliable, production-grade data pipelines from ingestion through delivery.
• Design and optimize data models that support internal analytics and customer-facing data products.
• Monitor, validate, and continuously improve data quality across production pipelines.
• Investigate data issues, perform root-cause analysis, and implement long-term solutions.
• Optimize SQL queries and pipeline performance to efficiently process large-scale datasets.
• Build robust monitoring, alerting, and automated validation processes.
• Collaborate closely with Data Scientists, Product Analysts, R&D engineers, Product Managers, and business stakeholders to deliver end-to-end data solutions.

Requirements

• B.Sc./B.A. in Industrial Engineering, Information Systems, Computer Science, or a related field.
• 5 years experience developing data transformations using Python and pandas.
• Strong SQL skills, including writing and optimizing complex queries.
• Experience building and maintaining production data pipelines
• Strong analytical and problem-solving skills.
• Familiarity with modern data engineering tools such as ETL orchestration and data modeling frameworks (e.g. Airflow, dbt).
• Experience with Git, CI/CD, or software engineering best practices.
• Experience working with cloud data warehouses (e.g. Snowflake, BigQuery, Redshift) — an advantage.