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We are seeking a QA Engineer with a strong passion for data quality, performance, and scale to join our Data Platform team.
This role is ideal for a QA professional who enjoys working close to complex data systems, understands large-scale pipelines, and wants to play a key role in shaping the automation and quality strategy of a data engineering organization.
You will act as the primary quality owner for high-volume, mission-critical data platforms, working closely with data engineers, backend developers, and platform teams.
What Youll Do:
Data Quality & Validation:
Design and execute data validation strategies for large-scale batch and streaming pipelines
Ensure data correctness, completeness, freshness, and consistency across the data lake
Define and automate checks for schema changes, data drift, and data quality regressions
Performance & Scalability Testing:
Plan and execute performance and scalability tests for data pipelines and processing jobs
Identify bottlenecks across ingestion, transformation, and querying layers
Partner with engineers to validate performance improvements and prevent regressions
Automation & Infrastructure:
Develop and maintain the data teams QA automation infrastructure
Build reusable testing frameworks and tools tailored for large datasets and pipelines
Integrate automated tests into CI/CD pipelines and production monitoring workflows
Collaboration & Ownership:
Work closely with data engineers, backend developers, and platform engineers throughout the development lifecycle
Act as the sole QA owner within a cross-functional team, driving quality without becoming a bottleneck
Participate in design discussions to ensure testability and observability are built in from the start
Quality Mindset & Communication:
Champion a quality-first culture within the team
Clearly communicate risks, findings, and quality metrics to technical stakeholders
Balance thoroughness with pragmatism in fast-moving, high-scale environments.

we are looking for a talented and Senior Data Engineer to join our data group.
The team is responsible for processing and transforming data from multiple external sources while building and maintaining an internal serving platform. Our key challenges include operating at scale, integrating with diverse external interfaces, and ensuring the data is served in a consistent and reliable manner.
Responsibilities:
Designing and implementing data platform
Transforming, modeling, and serving all medical data.
Utilize data best practices, to improve the product and allow data-driven decision-making.
Collaborate closely with cross-functional teams to understand business requirements and translate them into data-driven solutions.
Stay updated with the latest research and advancements in the data field.

Were seeking a passionate, experienced Senior Data Analyst to join our team.
Our offices are located in Poleg, Netanya, and we work in a hybrid model.
Responsibilities:
Drive strategic insights by working hands-on with complex data sets, seamlessly blending structured quantitative metrics with unstructured qualitative data (e.g., text, consumer feedback).
Build agile analytical solutions, developing and maintaining advanced visual reports and interactive dashboards in Power BI to allow stakeholders to consume complex data easily.
Deliver high-impact presentations (in English) to cross-functional teams and senior leadership, transforming raw data into clear, coherent, and compelling narratives that drive action.
Optimize and automate: Proactively identify bottlenecks in current research processes and reduce turnaround times by building automated pipelines, templates, and dynamic models using SQL and Advanced Excel.
Act as a strategic partner: Work highly autonomously to map business needs, master new domains on the fly, and provide data-driven guidance. This is an individual contributor role.

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).

Our Data team consists of highly skilled senior software and data professionals who collaborate to solve complex data challenges. We process billions of records daily from multiple sources using diverse infra and multi-stage pipelines with intricate data structures and advanced queries, and complex BI.

A bit about our infrastructure. Our main databases are Snowflake, Iceberg on AWS, and Trino. Spark on EMR processes the huge influx of data. Airflow does most of the ETL.

The data we deliver drives insights both for internal and external customers. Our internal customers use it routinely for decision-making across the organization, such enhancing our product offerings.

What Youll Do
Build, maintain, and optimize data infrastructure.
Contribute to the evolution of our AWS-based infrastructure.
Work with database technologies - Snowflake, Iceberg, Trino, Athena, and Glue.
Utilize Airflow, Spark, Kubernetes, ArgoCD and AWS.
Provide AI tools to ease data access for our customers.
Integrate external tools such as for anomaly detection or data sources ingestion.
Use AI to accelerate your development.
Assures the quality of the infra by employed QA automation methods.

As a Senior Product Analyst, you will partner closely with Product Managers, Engineering, Design, and Business stakeholders to turn data into actionable insights. Youll define success metrics, analyze product performance, and influence product strategy and prioritization across platform.
What Youll Do
Own and develop product KPIs to track usage, engagement, retention, conversion, and growth
Analyze user behavior and product performance to identify opportunities, risks, and improvement areas
Design, execute, and analyze A/B tests and experiments, from hypothesis to actionable recommendations
Partner with Product Managers to support roadmap planning and feature prioritization based on data and ROI
Perform deep-dive analyses on funnels, cohorts, and user segments
Leverage AI-powered analytics tools (e.g., Cursor, ChatGPT, AI copilots) to accelerate SQL, analysis, documentation, and insight generation
Clearly communicate insights and recommendations to product teams and senior leadership

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.

Analytics team lead | Fintech | Direct Placement Location: Tel Aviv | Hybrid Model We are seeking a strategic and impact-driven Analytics team lead to join a fast-growing Fintech company. This is a Direct Employment opportunity from day one, offering a leadership role in a highly dynamic and evolving business environment.
Key Responsibilities As the team lead, you will orchestrate the sprint cycles and backlog for a diverse group of over 5 analysts, ensuring high-speed delivery in a fast-paced setting. You will be responsible for cultivating analytical rigor through advanced A/B testing methodologies and standardized frameworks, while continuously auditing internal workflows to eliminate manual inefficiencies. Your role involves defining and owning departmental North Star metrics, proactively educating stakeholders on business drivers, and maintaining a hands-on approach to ensure complex, cross-functional projects meet their milestones. Additionally, you will champion the integration of AI tools and frameworks to accelerate the teams ability to generate rapid, actionable insights.

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.

we are looking for a Senior Data & Business Analyst to lead analytics and business health indicators across our operational and company-wide domains. This is a senior individual contributor role combining strategic thinking, deep hands-on analytical execution, and strong cross-functional business partnership. Youre welcome to work in our office in Tel Aviv.
The role requires the ability to operate at two levels simultaneously: understanding the full business picture while diving deep into complex datasets and technical execution. You will work closely with Operations, Finance, and other cross-functional teams to improve efficiency, transparency, and decision-making across the organization.
Your responsibilities will include:
Own end-to-end analytics for core operational processes.
Define, build, and maintain company-level operational and business health KPIs.
Translate ambiguous, cross-functional business questions into structured analytical frameworks.
Write advanced, high-performance SQL to extract, model, and analyze complex datasets.
Lead deep-dive analyses to uncover operational inefficiencies, financial impact, and process gaps.
Connect data across systems and explain upstream and downstream business impact.
Independently identify high-impact problems worth solving, not only respond to requests.
Drive cross-functional analytical initiatives at a company-wide level.
Present insights, trade-offs, and recommendations to senior leadership.
Synthesize complex findings into concise executive-level narratives.
Influence decisions and drive measurable impact based on data.
Act as a central analytical partner across multiple business domains.
Lead initiatives and move stakeholders forward without direct authority.

seeking a skilled and motivated Data Scientist. This individual will play a pivotal role in identifying key data points for collection, developing strategies to accumulate data and deriving actionable insights an anomaly based on a solid foundation of relevant know-how. Also, will also be responsible for creating, testing, and deploying scripts and methods for data collection and analysis to support decision-making. The Engineer will collaborate with cross-functional teams to identify critical data sources to determine the most effective data collection strategies, will develop automated and scalable data collection pipelines, will ensure data quality, integrity, and consistency across all sources and may use AI techniques to refine the results toward failures predictions.