Staff Engineer — Data Platform

Taboola · telaviv

Realize your potential by joining the leading performance-driven advertising company! As a Staff Engineer - Data Platform on the infrastructure department in ourTel Aviv Office, you’ll play a vital role in as a Staff Engineer – Data Platform in the Infrastructure Department in our Tel Aviv office, you’ll play a vital role in building the core data platform that powers Taboola’s reporting, billing, ML training, experimentation, revenue optimization, and business decision-making — ensuring data is reliable, fresh, scalable, and easy to use. You’ll be a senior technical leader across the Data Platform group, owning the hardest technical challenges across Spark, Iceberg, Kafka, and StarRocks. As a hands-on individual contributor, you’ll drive technical excellence and make every engineer around you better through mentoring and technical leadership. 1.4 billion users. 950,000 requests per second. 150TB of fresh data every day. 800M metrics every minute. That's the scale Taboola's Data Platform group operates at.   To thrive in this role, you'll need: 10+ years of hands-on software engineering, with deep expertise in Java or an equivalent JVM/object-oriented language Deep, hands-on expertise in Apache Spark — internals, Spark SQL and the Catalyst optimizer, and performance and cost tuning at scale Production experience with Apache Iceberg (or another open table format such as Delta Lake or Hudi) — table design, partitioning, compaction, and metadata/catalog management Deep expertise in Apache Kafka — internals, partitioning and delivery semantics, and operating high-throughput pipelines in production Hands-on experience with StarRocks or a similar MPP/real-time analytics engine (Druid, ClickHouse, Trino) Lead by example with AI: introduce agent-based engineering workflows, measure their impact, and help the group adopt them A track record of designing, building, and owning large-scale distributed data systems end to end — from architecture to production reliability Proven experience mentoring engineers and growing technical leaders — through design reviews, code reviews, pairing, and knowledge sharing Ability to influence without authority and drive alignment across teams, managers, and stakeholders Highly proficient with Linux and Kubernetes in production B.Sc. in Computer Science or equivalent experience Bonus points if you have: Contributions to open-source data projects (Spark, Iceberg, Kafka, StarRocks, or similar) Conference talks, blog posts, or other public technical writing Experience running Spark on GPU (RAPIDS) or other query acceleration techniques Experience with stream processing (Spark Structured Streaming, Kafka Streams, Flink) Experience with dbt and/or Airflow Knowledge of Ansible, Puppet, or similar configuration systems A track record of leveraging AI agents to accelerate engineering work Familiarity with how ML training pipelines consume data, to partner effectively with algo teams How you’ll make an impact: Shape the technical direction of the Data Platform group — the architecture, the long-term roadmap, and the bets we make on emerging tech (Iceberg, StarRocks, GPU Spark, dbt, AI tooling) Mentor and grow engineers across the group: lead design reviews, pair on hard problems, review code with care, and help strong engineers grow into technical leaders Raise the engineering bar group-wide — set standards for design, code quality, testing, observability, and operational excellence Own the hardest problems on our stack: Spark performance and cost across 60,000+ jobs/day, Iceberg table management at Taboola scale, high-throughput Kafka ingestion, and low-latency analytics on StarRocks Evolve our Spark SQL platform and the core framework code used across R&D — making the call on what gets built, refactored, or retired Drive the evolution of our lakehouse, with Iceberg as the backbone — from ingestion through storage, compaction, and serving Be the go-to expert for the production health of the main Taboola data pipeline — the system behind reports, ML model training, billing, and other business-critical processes — and lead deep-dives and postmortems on the toughest incidents Partner with team leaders, group managers, and fellow principal engineers to shape how the platform evolves across R&D, balancing the needs of algo, product, finance, and infrastructure stakeholders Stay deeply hands-on — write production code, build prototypes, and prove out new technology before the group commits to it Represent the group internally and externally — in architecture forums, hiring, open-source communities, and conference talks Directly shape how billions of people discover the internet   Here are some of the things engineers in our group have done — or are doing right now Pioneered deployment of Spark SQL workloads on GPU with RAPIDS Delivered a real-time pipeline reports product serving all our customers on top of Druid Actively migrating a proprietary ETL system to dbt-core Pioneered usa

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