Data Engineer

A market-leading, highly profitable enterprise Cybersecurity and Big Data technology corporation publicly traded on the US exchange.

Operating with thousands of employees worldwide and a premier R&D center in Israel, the company delivers mission-critical Data Security platforms protecting vast enterprise data assets, uncovering insider threats, and preventing data exfiltration across thousands of global corporations and institutional clients.

The company combines long-term commercial stability and exceptional corporate benefits with cutting-edge engineering challenges involving petabyte-scale data flows, processing billions of events in production.

Located in the Central / Sharon District, operating on an exceptionally flexible hybrid model with 3 days working from home.

Role Description-

• Serving as a Senior Data Engineer (Databricks Specialist), joining the core Data Engineering squad to drive a flagship, company-wide architectural transformation from SQL-based architectures to a modern Databricks lakehouse ecosystem.
• Architecting, developing, and optimizing high-throughput, production-grade ETL / ELT pipelines utilizing Python and PySpark to process over one billion events at scale.
• Designing and implementing resilient, end-to-end data pipelines and analytical storage models that empower mission-critical security analytics and threat-detection engines.
• Driving data optimization, partition strategies, and performance tuning across distributed compute clusters within Databricks.
• Establishing high engineering standards, automated testing frameworks, and continuous data quality monitoring for large-scale production data workflows.
• Collaborating cross-functionally with backend architects, security data researchers, and platform teams to build scalable, fault-tolerant data solutions.

Requirements-

• 4 years of proven hands-on experience as a Data Engineer – Mandatory
• Deep, proven practical experience developing and optimizing data pipelines in Databricks – Mandatory
• Advanced programming mastery in Python (clean code, OOP, and data processing libraries) – Mandatory
• Extensive, production-grade experience with PySpark for large-scale distributed data processing – Mandatory
• Strong hands-on background architecting robust ETL/ELT data pipelines handling massive volumes of complex events – Mandatory
• Solid understanding of distributed computing paradigms, data modeling, and query performance tuning – Mandatory
• Strong team collaboration mindset and fluent professional communication in English (written and verbal) – Mandatory