Senior Engineering Manager - Data Services
New
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LivePersonConversational AI
Fully RemoteFull-TimeManager
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 10+ years in software/data engineering, with strong backend or data platform experience; 5+ years managing engineering teams, including globally distributed organizations
- 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
- 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
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)
- Lead migration from legacy Hadoop/Spark environments to modern platforms such as Databricks
- Define and enforce SLOs/SLAs across the data platform
- Act as a key engineering partner to Product, Analytics, and Go-To-Market teams
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