Senior Engineering Manager - Data Services
New
Globally distributed across Europe, Israel, and IndiaFull-TimeManager
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Languages
- English
- Experience
- 10+ years in software/data engineering, with strong backend or data platform experience; 5+ years managing engineering teams
- Required Skills
- AgileElasticSearchKafkaKubernetesCassandraSparkDatabricks
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, or a related field
- 10+ years in software/data engineering, with strong backend or data platform experience
- 5+ years managing engineering teams, including globally distributed organizations
- Strong skills in Agile methodologies
- Strong communication skills (including English language skills) and organizational skills
- Proven ability to hire, grow, and retain high-performing teams
- Strong background in distributed systems and large-scale data platforms
- Hands-on experience with streaming systems (e.g., Kafka)
- Hands-on experience with NoSQL databases (e.g., Cassandra)
- Hands-on experience with search/indexing systems (e.g., Elasticsearch)
- Deep understanding of real-time vs batch processing trade-offs
- Experience with big data frameworks (Spark, MapReduce)
- Exposure to modern data platforms such as Databricks or lakehouse architectures
- Experience driving platform migrations or modernization initiatives
- Experience operating microservices in Kubernetes environments (GKE preferred)
- Strong focus on reliability, scalability, and observability
- Strong cross-functional communication and highly organized collaboration skills
- Proven decisiveness in establishing clear ownership boundaries and sticking to decisions
- Discipline to document and reference all decisions
- Ability to influence stakeholders across engineering, product, and business teams
Responsibilities
- Build, lead, and grow a high-performing, distributed team across EU, Israel, and India
- Drive a culture of ownership, accountability, and operational excellence
- Evolve the architecture for near-real-time data platforms
- Lead design decisions across streaming, storage, indexing, and access layers
- Ensure systems are scalable, resilient, and aligned with long-term product strategy
- Partner with Product and Architecture to translate vision into executable roadmaps
- Drive delivery of a large fleet of microservices running on Kubernetes (GKE)
- Ensure predictable, high-quality releases across a complex distributed system
- Lead migration from legacy Hadoop/Spark environments to modern platforms such as Databricks
- Balance innovation with reliability and business continuity
- Reduce system complexity while improving performance and cost efficiency
- Define and enforce SLOs/SLAs across the data platform
- Own reliability, observability, and incident response processes
- Build scalable on-call and production support models across time zones
- Act as a key engineering partner to Product, Analytics, and Go-To-Market teams
- Communicate technical strategy, trade-offs, and risks clearly to senior stakeholders
- Align platform capabilities with customer and business outcomes
View Full Description & ApplyYou'll be redirected to the employer's site
