Back End Developer

Perion is a global advertising technology company delivering solutions to the biggest brands and publishers around the globe across search, social media and display, video, CTV, and programmatic DOOH.

Home to an award-winning technology solution –– with our unique data-driven AI/ML based technologies, we deliver and optimize hundreds of terabytes of data and billions of events per day. We’re working with dozens of sources to provide a superior experience across screens and platforms, including mobile, video, social and native.

We are seeking for an experienced Go Developer, you will work on performance-critical backend services that power programmatic ad delivery, real-time bidding (RTB), and data synchronization. You’ll be developing high-concurrency, low-latency services in Go, ensuring our systems stay reliable and fast even under heavy load.

Key Responsibilities:

• Design and implement Go services for ad serving, bidding, auction logic, and user tracking at scale.
• Work with real-time data streams, impression logging, and event pipelines.
• Optimize systems for performance and throughput
• Build caching layers and efficient data access strategies (e.g., using Redis, Memcached).
• Collaborate with DevOps to ensure scalability, observability, and reliability of deployed systems.
• Write unit and integration tests to validate service functionality and performance.

Required Qualifications:

• 2–4 years of backend development experience, with at least 1–2 years using Go in production.
• Solid understanding of distributed systems and performance tuning.
• Proficiency in data storage technologies (e.g., Redis, PostgreSQL).
• Familiarity with programmatic advertising concepts (RTB, DSPs, SSPs, OpenRTB) is highly desirable.
• Experience with containerization tools like Docker.
• Strong understanding of network protocols (HTTP, gRPC) and request handling at scale.

Nice to Have:

• Previous experience in Ad Tech (e.g., ad exchanges, pixel tracking, cookie syncing).
• Experience with A/B testing platforms or real-time analytics.
• Exposure to user segmentation, frequency capping, and attribution modeling.
• Working knowledge of time-series databases and monitoring tools