Data Platform Engineering Team Leader

TL;DR

Data Platform Engineering Team Leader (Java/Kafka/Spark): Lead a team of 4–6 engineers building hirify.global’s core data platform for reporting, billing, ML training, experimentation, and revenue optimization with an accent on reliable, fresh, scalable data pipelines. Focus on setting technical direction and owning production health and reliability while evolving the Spark SQL platform and core framework code used across R&D.

Company

hirify.global builds data and advertising technology at massive scale, powering reporting, billing, and ML-driven optimization.

What you will do

• Set technical direction for the team: architecture, roadmap, and technology bets (e.g., Iceberg, StarRocks, GPU Spark, dbt, AI tooling).
• Own production health and reliability of the main data pipeline powering reports, ML training, billing, and other business-critical processes.
• Grow engineers through coaching, mentoring, 1:1s, and candid feedback to enable senior engineers to perform at their best.
• Prioritize across competing demands from algo, product, finance, and infrastructure stakeholders; drive cross-team alignment with peer leaders and principal engineers.
• Upscale the team with AI by introducing agent-based workflows and measuring impact on engineering velocity.
• Stay hands-on (30–40%): review designs and code, unblock the team, and represent the team in design reviews, postmortems, hiring, and technical forums.

Requirements

• Location: Tel Aviv, Israel
• 5+ years leading and managing software development teams.
• 8+ years engineering background in Java or an equivalent object-oriented language.
• Experience building large-scale distributed systems and understanding production infrastructure.
• Strong CS fundamentals: OOP design, data structures, and concurrent/multi-threaded programming.
• Deep expertise in Kafka and Spark, plus highly proficient Linux and Kubernetes in production; experience with both SQL and NoSQL systems.

Nice to have

• Experience with dbt and/or Airflow.
• Experience with StarRocks and/or Druid production deployments.
• Knowledge of Ansible, Puppet, or similar configuration systems.
• Track record of leveraging AI agents to accelerate engineering work.
• Familiarity with how ML training pipelines consume data to partner effectively with ML/algo teams.

Culture & Benefits

• Hybrid work schedule with 3 days in-office, with an option to come in more often if desired.
• Comprehensive benefits (including health) and location-specific perks (e.g., gym partnerships, parking).
• Fully stocked kitchen and emphasis on well-being.
• Work with large-scale systems and collaborate with engineers familiar with Kafka, Cassandra, Airflow, and Spark.