Required Data Engineering Manager
Data, Ramat Gan, Israel (Hybrid)
Description
We are one of the most popular and downloaded apps in the world. Working with us provides a unique opportunity to influence hundreds of millions of our users and to be part of the journey that makes us a super-app. Our mission is to make peoples lives easier by enabling meaningful connections, from precious moments with family and friends, through managing business relationships to pursuing their passions.
As an Engineering Manager in the data department, youll build and scale our data platform and data apps that powers our business insights. Youll design and implement robust pipelines to process billions of daily records, leveraging cutting-edge cloud technologies to transform data into actionable intelligence.
If youre passionate about data engineering and driving business growth through insights, wed love to hear from you!
Responsibilities:
Lead and grow Data Engineering and Machine Learning teams in a high-scale environment (tens of billions of events per day).
Own the design and evolution of a self-service data platform enabling internal teams to easily build, ship, and consume data products.
Architect and scale batch and streaming pipelines powering core business and ML use cases.
Drive production ML systems end-to-end (recommendation, ranking, prediction) with direct business KPI impact.
Ensure reliability, scalability, and observability of large-scale data and ML systems in production.

We are seeking an experienced and highly motivated Team Lead to lead our Data Infrastructure team. This role is responsible for managing a team of engineers while driving the design, scalability, reliability, and operational excellence of the companys core data platform.
The ideal candidate combines strong technical expertise in distributed systems and data technologies with proven leadership capabilities, strategic thinking, and the ability to lead complex initiatives end-to-end - from initial design and planning through implementation, deployment, and production ownership.
Key Responsibilities:
Lead, mentor, and develop a team of Data Infrastructure engineers, fostering a culture of ownership, collaboration, and continuous improvement
Define and execute the technical roadmap for the organizations data infrastructure platform
Oversee the architecture, scalability, reliability, and performance of large-scale data systems and pipelines
Define and drive data quality standards, monitoring, and validation processes to ensure trust and reliability across the data platform
Partner with engineering and product teams to enable scalable data foundations for analytics, machine learning, and AI-driven initiatives
Lead cross-functional technical initiatives end-to-end, including requirements gathering, architecture design, implementation, deployment, and post-production ownership
Partner closely with cross-functional teams including Data Engineering, Analytics, Backend Engineering, DevOps, and Product Management
Drive operational excellence through monitoring, observability, automation, and incident management best practices
Ensure high availability and stability of critical production systems
Lead technical design discussions, architecture reviews, and engineering planning processes
Establish and promote engineering standards, development best practices, and quality assurance processes
Manage project prioritization, resource allocation, and timely delivery of strategic initiatives
Support recruitment, onboarding, performance management, and career development of team members
Evaluate emerging technologies and recommend improvements to the data platform ecosystem

As an MLOps Team Leader, you will own our AI infrastructure and backend engineering efforts, leading a team at the intersection of systems design and machine learning to build the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features.

What Youll Be Doing:
Lead MLOps Engineering: Own the roadmap and execution for a team of backend and AI infrastructure/MLOps engineers, setting technical direction, removing blockers, and holding the bar on delivery quality.
Build Production AI Systems: Design and implement production-grade, end-to-end AI solutions, including agentic workflows, that integrate Deep Learning models and Computer Vision algorithms into real features. Own the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Architect Data Platforms: Drive the design and implementation of scalable data platforms and pipelines that power our AI capabilities reliably and with an eye on cost and scale.
Cross-functional Collaboration: Work closely with the Algorithms and Data teams to tackle complex, real-world problems, translating research into shippable, maintainable systems.
Elevate Engineering Standards: Champion Software Engineering and System Design best practices across the group, introducing the right methodologies, tooling, and culture of craft.

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.
What your team owns
The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:
1. Content-moderation inference at scale
Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.
2. GenAI safety & red-teaming
Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming our company's leading edge into agentic-AI safety.
3. Data platform & MLOps
An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.
What you'll do
Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.
Leadership competencies
People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

We are looking for a Software Engineer to help design and develop the core services and applications that drive our data capabilities. In this role, you will build high-quality, scalable software; contribute to the architecture of our next-generation data systems; and collaborate with research and product teams to turn cutting-edge ideas into production-ready features.
Responsibilities:
Design, develop, and maintain scalable backend services and components that power our data workflows, analytics, and product features.
Build high-quality internal tools and applications that improve engineering workflows and enable data-driven development across the organization.
Contribute to the architecture and evolution of our new data platform with a strong focus on clean design, testability, maintainability, and performance.
Collaborate closely with security researchers, analysts, and product managers to translate innovative cybersecurity concepts into reliable, production-ready software.
Apply engineering best practices across the stack - including code quality, testing, observability, versioning, and documentation - to ensure system robustness as we scale.

Required Senior Data Engineer
About the role:
You will specialize in designing and building world class, scalable data architectures, ensuring reliable data flow and integration for groundbreaking biotechnological research. Your expertise in big data tools and pipelines will accelerate our ability to derive actionable insights from complex datasets, driving innovations in improving patients outcome and in delivering life savings treatment solutions.
In this role, you will work closely with data scientists, AI/ML engineers, architect and other cross-functional teams to understand their data needs and requirements. You will also be responsible for ensuring that data is easily accessible and can be used to support data-driven decision making.
Location: Ramat Gan, (Hybrid model)
What will you do?
Design, build, and maintain data pipelines that ingest, transform, and load noisy, heterogeneous biological data at scale - from single-cell sequencing, genomic, and clinical sources - across databases, APIs, and flat files
Enhance our data warehouse system to dynamically support multiple analytics use cases
Implement and productize complex scientific and computational algorithms as scalable, production-grade pipeline components
Drive our data infrastructure toward becoming AI-native, enabling AI/ML systems and agents to reliably query, reason over, and act on our data
Implement data governance policies and procedures to ensure data quality, security, and privacy
Collaborate with ML and AI scientists and other cross-functional teams to understand their data needs and requirements
Drive team capability growth by championing an AI-first SDLC, mentoring data engineers on AI-assisted development practices and tooling
Develop and maintain documentation for data pipelines, processes, and systems
Requirements
We will only consider senior data engineers that have demonstrated strong system design skills combined with an extensive background working with data orchestration, data warehousing and ETL tools.

we are looking for a Big Data Engineer.
As a Senior Big Data Engineer, working within Mobility Group, you will play a pivotal role in designing, developing, and maintaining the data infrastructure that powers our location analytics platform.
RESPONSIBILITIES:
Data Pipeline Architecture and Development: Design, build, and optimize robust and scalable data pipelines to process, transform, and integrate large volumes of data from various sources into our analytics platform.
Data Quality Assurance: Implement data validation, cleansing, and enrichment techniques to ensure high-quality and consistent data across the platform.
Performance Optimization: Identify performance bottlenecks and optimize data processing and storage mechanisms to enhance overall system performance and reduce latency.
Cloud Infrastructure: Work extensively with cloud-based technologies (GCP and AWS), to design and manage scalable data infrastructure.
Collaboration: Collaborate with cross-functional teams including Data Analysts, Data Scientists, Product Managers, and Software Engineers to understand requirements and deliver solutions that meet business needs.
Data Governance: Implement and enforce data governance practices, ensuring compliance with relevant regulations and best practices related to data privacy and security.
Monitoring and Maintenance: Monitor the health and performance of data pipelines, troubleshoot issues, and ensure high availability of data infrastructure.
Mentorship: Provide technical guidance and mentorship to junior data engineers, fostering a culture of learning and growth within the team.

Join our mission to build and scale the engineering platform behind a leading SaaS cybersecurity solution trusted by enterprises worldwide.
We are looking for a passionate DevOps Group Lead. You'll lead a strategic engineering organization responsible for the platforms, infrastructure, and automation that power our software delivery lifecycle. You'll manage four Team Leaders and their engineering teams, driving innovation across CI/CD, Kubernetes, AWS, developer platforms, and cloud operations while enabling fast, secure, and reliable software delivery.
This is a leadership role for someone who combines strong technical expertise with exceptional people leadership and a passion for building high-performing engineering organizations.
How will you make an impact?​
Lead, mentor, and develop 3 Team Leaders and their engineering teams, fostering a culture of ownership, collaboration, and continuous learning.
Define and execute the DevOps and Platform Engineering strategy supporting a large-scale SaaS cybersecurity platform.
Own the architecture, scalability, availability, security, and operational excellence of our cloud infrastructure and engineering platforms.
Drive the evolution of modern CI/CD pipelines, Kubernetes platforms, Infrastructure as Code, GitOps, and developer self-service capabilities.
Partner closely with Engineering, Product, Security, Architecture, and R&D leadership to accelerate software delivery while maintaining the highest standards of security and reliability.
Lead cloud-native initiatives with a strong focus on AWS, Kubernetes, automation, and platform standardization.
Establish engineering metrics, operational KPIs, SLOs, and continuous improvement initiatives across the organization.
Champion DevSecOps best practices and embed security into every stage of the software delivery lifecycle.
Lead organizational planning, hiring, budgeting, prioritization, and execution while supporting the growth and development of future engineering leaders.

Required Senior ML Data Engineer
About the team:
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

We are looking for a highly driven and experienced AI Engineer Team Leader to manage and mentor a core team of engineers while remaining 50% hands-on. In this critical role, you will lead the development of our enterprise Agentic AI platform, blending deep technical expertise in AI agent development with strong engineering leadership.
As a Team Leader, you will drive the technical agenda, plan sprints, and collaborate closely with Product Managers and cross-functional partners to turn powerful capabilities into reliable, safe, and fast product experiences. You must thrive in a high-pressure, dynamic startup environment characterized by a fast pace and frequent pivots, ensuring your team remains focused, agile, and aligned with business goals while executing at the highest level.
You won't just manage - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily, and iteration in front of enterprise customers.
Responsibilities
Team Leadership & Management (50%): Manage, mentor, and grow a team of AI and software engineers. Oversee task allocation, sprint planning, and daily agile ceremonies.
Hands-On Engineering (50%): Actively code, design, and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Technical Strategy & Architecture: Drive the technical agenda, architecture decisions, and best practices for developing scalable, LLM-driven applications grounded in our knowledge graph.
System Integration: Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Production Excellence: Build evaluation harnesses, guardrails, and safety/bias checks. Optimize agents for latency, cost, and reliability at enterprise scale. Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Cross-Functional Collaboration: Work intimately with Product Management, Data, and Design teams to translate customer needs into agent capabilities, define roadmaps, and prioritize tasks.
Agility & Execution: Lead the team through frequent pivots and rapid development cycles, ensuring high-quality delivery under pressure in a fast-paced startup environment.