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we are seeking a Senior Data Infra Engineer. You will be responsible for designing and building all data, ML pipelines, data tools, and cloud infrastructure required to transform massive, fragmented data into a format that supports processes and standards. Your work directly empowers business stakeholders to gain comprehensive visibility, automate key processes, and drive strategic impact across the company.

Responsibilities

Design and Build Data Infrastructure: Design, plan, and build all aspects of the platform's data, ML pipelines, and supporting infrastructure.
Optimize Cloud Data Lake: Build and optimize an AWS-based Data Lake using cloud architecture best practices for partitioning, metadata management, and security to support enterprise-scale operations.
Lead Project Delivery: Lead end-to-end data projects from initial infrastructure design through to production monitoring and optimization.
Solve Integration Challenges: Implement optimal ETL/ELT patterns and query techniques to solve challenging data integration problems sourced from structured and unstructured data.

We are seeking a Senior Data Engineer to join our R&D organization as part of a backend-oriented team, responsible for building and scaling the core data infrastructure.
In this role, you will design and develop data pipelines that stream and process data directly from production systems. You will play a key role in shaping our data platform, building robust, scalable infrastructure and pipelines using modern technologies, and working hands-on with both new components and existing systems.
Responsibilities
Collaborate as a strong team player within a dynamic, cross-functional environment
Design, develop, and maintain scalable data models, Lakehouse architectures, pipelines, and ETL processes
Enhance data workflows to support efficient real-time and batch processing
Work closely with cross-functional teams to understand data requirements and deliver impactful solutions
Stay up to date with the latest data engineering technologies and best practices, continuously improving our data platform.

We are looking for an ML Engineer / MLOps Tech Lead to promote machine learning engineering excellence. Someone who is passionate about building scalable, high-quality data products and processes, while ensuring production systems maintain strong real-time performance observability.
You will focus on designing and maintaining the core infrastructure that empowers the Machine Learning Engineers working within Data Science product teams. Youll collaborate closely with stakeholders across data science, product, and engineering, playing a pivotal role in driving the business by architecting and enabling the infrastructure for machine learning model development, serving, and lifecycle management-the foundation of our product.
Responsibilities:
Collaborate with product, data science, and engineering teams to solve complex problems, identify trends, and create opportunities through robust ML infrastructure.
End-to-end ML delivery - enabling model performance development, training, validation, testing, and version control.
Build and support monitoring and observability tools - dashboards, alerts, and performance tracking of models in production.
Lead architecture projects such as: Feature Store, Vector / Graph Databases.
Data wrangling - supporting and enabling data requirements for research, training, validation, and testing.
Drive engineering best practices including code and model versioning, CI/CD pipelines, rollout strategies, and disaster recovery procedures.

Were looking for a Senior Data Scientist to take full ownership of high-impact ML problems within our DSP platform.
You will work on core bidding, budget optimization, and prediction systems that operate at massive scale and strict latency constraints. This role combines deep modeling expertise, experimentation rigor, and strong product intuition.
You will be expected not only to build models - but to define problems, challenge assumptions, and drive measurable business impact.
What Youll Do:
Own end-to-end ML projects: problem definition → research → modeling → offline validation → production deployment → online A/B testing → impact analysis.
Develop and improve models for: Bid optimization , Conversion rate / pLTV prediction, Budget pacing and allocation, Auction dynamics & win-rate modeling.
Analyze large-scale, high-dimensional auction and user-level datasets to extract actionable insights.
Design robust feature engineering pipelines across behavioral, contextual, and advertiser-level signals.
Improve model performance under real-time constraints (low latency, high throughput).
Lead experimentation design and statistical validation of online tests.
Collaborate closely with engineering and product to translate research into scalable production systems.

At our company, Israels first fully digital bank, were building the technology that powers a new era of intuitive, transparent, people-first banking. As our platform grows and our services expand, were looking for a team leader to lead a data engineering team.
The team is responsible for ingestion, processing, and serving bronze level data to other analytic teams in the organization.
In this role, you will define the data architecture of our unified Data Lakehouse, own all ETL and streaming operations, define governance processes and implement tools, make sure our data is fresh, accurate and whole always.
Your Day-to-Day
Menor and lead a team of 4-6 data engineers.
Own sensitive data operations, including monitoring, on-call and production operations.
Build and manage all ETL and streaming operations related to the Data Lakehouse.
Develop, maintain, and optimize robust data pipelines and integrations across multiple systems.
Build a platform for other analytic teams to build data products on top of the Data Lakehouse.
Define and implement quality and governance processes to make sure data is fresh, accurate and whole.
Collaborate with engineering, BI, and business teams to translate requirements into scalable data solutions
Work hands-on with data orchestration, transformation, and cloud infrastructure (OCI, Snowflake, AWS)
Support implementation of best practices in data management and observability.

We are looking for aData Scientist to design and implement the decision-making logic of our Price Optimizer. You will focus on translating complex business rules into mathematical models and production-ready Python code, supported by a dedicated Data Engineering team that handles the underlying infrastructure.
Your primary mission is to build the "brain" of our system. You will work at the intersection of Data Science and Product, ensuring our simulation and optimization engines accurately reflect real-world pricing strategies and market dynamics. While you will write production code, you will rely on our Data Engineers for ETL pipelines orchestration and distributed compute scaling.
Responsibilities:
Implement Business Logic: Translate intricate pricing rules and commercial strategies into robust Python code and mathematical constraints.
Refine Optimization Models: Develop and tune the simulation and revenue management algorithms that drive our pricing recommendations.
Write Clean Code: Contribute high-quality, tested, and maintainable code to the core logic repositories.
Analyze & Improve: Use data to validate model behavior and identify edge cases where business rules clash with algorithmic outputs.
Collaborate: Partner with Solution Architects to define logic requirements and with Data Engineers to integrate your models into the Dagster pipelines.

We are seeking a Senior Data Analyst to serve as the primary interface and strategic analytics partner between Data Engineering / DWH teams, management, and business leaders. In this strategic role, you will work closely with leadership and business partners to translate business objectives into actionable data solutions, establish analytics standards and best practices, and lead experimentation across core business processes. You will also plan, prioritize, and manage analytics initiatives and roadmaps, ensuring the timely delivery of insights and solutions that drive informed decision-making across Marketing, Sales, Finance, Support, and Customer Success.
Key Responsibilities
Serve as the primary interface and analytics partner between Data Engineering / DWH teams and business stakeholders
Translate business needs into analytics requirements, specifications, roadmaps, and prioritized backlogs
Collaborate with Data Engineers on DWH requirements, data models, and pipelines
Plan, coordinate, and track analytics initiatives and projects to ensure timely delivery and alignment with business objectives
Support stakeholders in understanding, interpreting, and using ingested and production data
Define and own analytics standards, metrics, and best practices across the company
Design, execute, and analyze experiments to optimize core business processes
Define dashboard requirements and deliver impactful dashboards and insights
Work with Systems, Product, and R&D teams to map data sources, address data gaps, and improve data quality.

We are looking for a visionary and technically versatile Data Science Manager to join the Data Science & MLE group within our DS & Analytics organization.
In this pivotal role, you will lead a hybrid team of Data Scientists and Machine Learning Engineers, acting as the bridge between cutting-edge technical innovation and high-level business strategy. You won't just be managing a team; you will be the Technical Lead defining how we solve complex problems-from predicting player behavior to optimizing marketing budgets using the latest in Generative AI.
You will partner closely with Game Directors, Product Managers, and our central analytics teams to drive value across Zynga's diverse portfolio. If you are a leader who is "bilingual" in data-possessing deep Data Science expertise to guide methodology while bringing high MLE and Engineering skills to build scalable, production-ready systems-we want to hear from you.
Key Responsibilities:
Team Leadership & Mentorship: Recruit, retain, and develop top-tier talent within the Data Science & MLE team. Foster an inclusive culture of innovation where technical rigor meets creative problem-solving.

Technical Direction & Engineering Standards: Act as the hands-on Technical Lead for the domain. You will supervise the end-to-end development lifecycle-from research to production-enforcing high standards and MLOps to ensure our models are scalable and maintainable.
Strategic DS & Analytics Support: Drive the development of advanced analytical frameworks to solve core business challenges. You will guide the team in applying rigorous statistical and machine learning methods to areas such as Time Series Forecasting, Causal Inference, Marketing Optimization, Root Cause Analysis, and more.
Strategy & Collaboration: Partner with Game Directors, BI Platform PMs, and embedded analytics teams to define the data strategy and roadmap. You will ensure that our technical initiatives are directly aligned with company-wide goals and solving the right problems.
Global Alignment: Maintain a tight collaborative network with Data Science & Analytics managers in North America, ensuring global consistency in methodologies, knowledge sharing, and joint development.

We are looking for a Senior Analytics Engineer to help design and build the engineering foundation that powers analytics across the organization.
Our goal is to create a modern data environment where analytics development is fast, reliable, scalable, and increasingly automated. This includes building strong data warehouse foundations, scalable modeling layers, and introducing AI-powered tools and automation that accelerate how data products are built and used.
In this role, you will be part of an analytics squad, working closely with analysts and business stakeholders while building the infrastructure, automation frameworks, and intelligent tooling that enable analytics to scale across the organization.
This is a unique opportunity to help build the next generation of the data organization.
Key Responsibilities
Lead AI adoption in the analytics platform, building tools and workflows that automate analytics development, dashboards, and data exploration
Design and build scalable data warehouse models and transformation layers
Build and optimize ETL pipelines and core analytics infrastructure (Bronze / Silver)
Improve performance, reliability, and scalability of the analytics platform
Develop automation and internal tools that accelerate analytics workflows
Enable self-serve data access across the company through semantic layers and reusable datasets
Collaborate with analysts and business teams within an analytics squad.

We are looking for an experienced and hands-on Backend Engineer to be a key player in building high-scale Data Platforms and Products for our business teams.
This role involves working with large datasets and scalable systems, and developing internal tools to enable data-driven decision-making across the company.
Key Responsibilities:
Develop internal tools for various teams.
Build and maintain microservices and APIs to support diverse workflows.
Operate in a real-time, event-driven environment.
Create and manage data pipelines.
Take ownership of multiple systems and products.
Develop and deploy machine learning pipelines to production in an event-driven architecture.
Work in a multi-cloud environment (Azure/GCP/AWS).
Integrate third-party tools with our platform.
Translate business requirements into technical specifications.
Our Tech Stack:
Python, BigQuery, Redis, RabbitMQ, MySQL, Tornado, SQLAlchemy, Airflow, Airbyte, NewRelic, Elastic, Kubernetes (K8S).

As a Product Data Analyst, you will play a pivotal role in driving data-based decision-making and shaping product direction across the organization. You will oversee complex analytical initiatives, guide the development of KPIs and performance frameworks, and partner with product, engineering, and business leaders to influence strategic priorities.

we are looking for a Senior Data Scientist to build and deploy time series forecasting and classical machine learning models that support core business planning and operational decisions. You will own problems end-to-end, from data exploration and feature engineering to model deployment and monitoring, working with large-scale, real-world datasets.
The role emphasizes strong statistical thinking, robust modeling, and production reliability over experimentation with trendy frameworks. Your work will directly impact forecasting accuracy, resource planning, and key business metrics.
Youre welcome to work in our offices in Tel Aviv.
Your responsibilities will include:
Time series forecasting. Design, build, and maintain forecasting models (e.g., demand, usage, capacity). Evaluate performance using appropriate metrics and improve accuracy over time.
Classical ML modeling. Develop and deploy models using regression,Binary classifcatiom , tree-based methods, and other well-established approaches where they provide the best trade-off between performance, interpretability, and maintainability.
Feature engineering & data preparation. Build robust pipelines for data cleaning, transformation, and feature generation (including time-based features, seasonality, and external signals).
Model evaluation & monitoring.Define evaluation frameworks, backtesting strategies, and monitoring to ensure models remain stable and reliable in production.
Production & MLOps. Deploy models into production environments, collaborate on pipeline orchestration, and ensure reproducibility and version control.
Exploratory analysis. Analyze large datasets to identify patterns, anomalies, and drivers of change in time-dependent behavior.
Stakeholder collaboration. Work closely with business and product teams to define forecasting needs, align on success metrics, and translate model outputs into actionable insights.
Cross-domain contribution. Contribute to adjacent data science areas (e.g., LLM-based systems) as needed, ensuring shared ownership and continuity across team responsibilities.

we are looking for a Senior Data Scientist (Applied AI / LLM) to design, build, and deploy production-grade AI systems based on large language models. You will focus on turning LLM capabilities into reliable, measurable business solutions, while also contributing to predictive modeling across different business domains.
Your work will include building evaluation frameworks, improving model accuracy, and developing agent-based systems that automate analytical and data workflows (e.g., data quality monitoring). The role emphasizes production readiness, robustness, and real business impact-not experimentation.
Youre welcome to work in our office in Tel Aviv.
Your responsibilities will include:
LLM system development. Design and deploy LLM-based solutions (e.g., RAG pipelines, agent workflows) for real business use cases.
Evaluation & reliability. Build evaluation frameworks to measure accuracy, consistency, and failure modes of LLM systems. Continuously improve performance based on real-world usage.
Agent-based automation. Develop agentic solutions to automate workflows such as data quality checks, anomaly detection, and reporting.
Applied predictive modeling. Apply classical ML and statistical methods to business domains such as HR and Finance (e.g., forecasting, classification, risk modeling).
Production & MLOps. Deploy and maintain models in production, ensuring monitoring, versioning, and scalability.
Stakeholder collaboration. Translate business needs into AI/ML solutions and communicate trade-offs, risks, and performance clearly.
Cross-domain contribution. Contribute to adjacent areas (e.g., forecasting models) to ensure team redundancy and shared ownership of critical workflows.

Design, implement, and improve ML and AI models to drive business outcomes across multiple domains, such as recommendation systems, image recognition, ChatBot, etc.
Own the end-to-end ML lifecycle: data preprocessing, feature engineering, model training, validation, and deployment.
Expert level in designing evaluation pipelines to prove performance, scalability, and consistency
Leverage generative AI and LLMs to enhance existing workflows and explore new product opportunities.
Collaborate with Product, Engineering, and Analytics teams to align modeling efforts with business needs.
Clearly communicate complex findings and model insights to stakeholders.

we are where high-growth startups turn when they need to move faster, scale smarter, and make the most of the cloud. As an AWS Premier Partner and Strategic Partner, we deliver hands-on DevOps, FinOps, and GenAI support that drives real results.
We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.
Were looking for a Senior Data Architect to help shape how high-growth startups build and scale on AWS. In this role, youll design and deliver end-to-end data and analytics solutions - from architecture and pipelines to visualization and insights - guiding customers from concept through production. Youll work closely with startup founders, technical leaders, and account executives to create scalable, cost-efficient architectures that drive real business impact.
Work location - hybrid from Tel Aviv
If you are interested in this opportunity, please submit your CV in English.
Key Responsibilities
Design, develop, and implement data & analytics solutions to meet business requirements and create cost-efficient, highly available, and scalable customer solutions, including Well-Architected reviews and SoW.
Research and analyze current solutions and initiate improvement plans.
Collaborate with other engineers and stakeholders to ensure solutions are designed and developed according to best practices.
Lead workshops, POCs, and architecture reviews with startup customers, conferences, webinars, and more.
Stay up to date on Data Engineering and Analytics trends and contribute to internal enablement.
Frequent travels - locally (on-demand to meet with customers and partners and attend local events) and abroad (at least once a quarter).