Jobs at Lusha

Own end-to-end analytics foundations, including data models, pipelines, semantic layers, BI infrastructure, data quality, and observability. Partner with Data Science and business stakeholders to create trusted metrics supporting Product, GTM, and Finance. Mentor team members, establish engineering standards, lead cross-functional initiatives, and use GenAI to improve analytics development speed and accuracy. The role is hybrid in Tel Aviv with three office days weekly.

Own and evolve Lusha’s shared data platform across Databricks, Kafka, CDC, Airflow, Elasticsearch, AWS, Kubernetes, and databases. Build and operate reliable batch and streaming pipelines, improve performance and cost, establish governance and observability, automate manual processes, and lead cross-team architecture initiatives. The role also requires daily use of AI coding tools and building LLM, agent, and MCP-based platform workflows.

Own full sales cycle for mid-market and enterprise customers: prospect, map stakeholders, apply MEDDPICC, negotiate, and close deals. Leverage GenAI and AI tools to improve prospecting, deal strategy, and pipeline execution while meeting revenue targets in a hybrid Tel Aviv role.

Develop and deploy end-to-end machine learning models for personalized sales experiences. The role involves solving complex business problems with classic and advanced ML techniques, building agentic pipelines, working with product and engineering teams, and owning solutions from success criteria through monitoring and maintenance. Responsibilities include working with tabular, time-series, and text data, NLP, scalable ML pipelines, production deployment, and leveraging AI development tools.

Own high‑severity support escalations for a SaaS product, investigate API logs and integration workflows, run live customer sessions, coach the support team, close feedback loops with R&D, and build playbooks and automations using AI tools to prevent recurring issues.

Own and scale paid acquisition across LinkedIn, Google, and Meta to drive global pipeline and revenue. Set channel strategy, testing frameworks, and KPIs; use AI tools to optimize campaigns, collaborate with growth, sales, and RevOps, and potentially grow a team.

Lead PMM owning mid-funnel growth, pricing, and packaging to drive monetization, adoption, retention, and expansion. Define tiers, limits, and lifecycle journeys across product surfaces; partner cross-functionally and use data and AI workflows to optimize conversion and revenue.