Top Data Analyst Jobs in Israel

Analyze large player datasets to uncover behavioral insights, evaluate virtual economies and incentives, and guide product and business strategy. The analyst will conduct research, develop hypotheses and analytical frameworks, translate complex findings into persuasive recommendations, and partner with product, design, and executive teams. The role emphasizes strategic thinking and insight generation rather than dashboard creation or data pipeline maintenance.

Own product analytics for a consumer AI browser across acquisition, activation, engagement, retention, and monetization. Analyze large-scale behavioral data, define product metrics, design and evaluate A/B tests, investigate user behavior, and translate findings into product decisions. Partner closely with Product, Engineering, Growth, and leadership while building analytical frameworks and self-service tools. Use AI tools to accelerate analysis and automation.

Analyzes customer and e-commerce performance using web analytics, SQL, BI tools, and large datasets. Defines and validates event tracking, monitors conversion funnels and KPIs, identifies trends and issues, supports feature launches, and delivers actionable insights. Collaborates with product, R&D, professional services, and BI teams to ensure data integrity and reliable dashboards.

Analyze large datasets to understand player behavior, optimize retention and game revenue, and recommend product improvements. Build dashboards and reports for business metrics, apply statistical methods and A/B testing, and collaborate with developers and product teams to implement features that improve user experience. The role requires strong SQL, analytical expertise, stakeholder communication, and familiarity with AI tools and Python-based analytics.

Analyzes data from multiple sources, builds BI reports and dashboards, identifies trends and anomalies, performs ad hoc analyses, and translates findings into actionable business recommendations. Uses SQL and visualization tools to support data-driven decisions, improve reporting and data quality, and collaborate with technical and business stakeholders.

Own data analysis, user acceptance testing, dashboards, KPI tracking, production support, and data quality investigations for the Pioneer data platform. Partner with analysts, product management, and engineering to deliver reliable data products, troubleshoot issues, and identify improvements. The role requires SQL and scripting knowledge, analytical independence, strong communication, and experience working with large-scale data platforms.

Lead cybersecurity asset intelligence research across device, identity, software, and SaaS data. Analyze large-scale security datasets, automate workflows with Python and SQL, integrate AI/LLM tools, and develop asset classification, fidelity, risk, and criticality logic. Collaborate with Product and Engineering teams to translate research into production-ready cybersecurity capabilities.

Analyze product and growth data across signup, activation, feature adoption, retention, and launches. Partner with Product and Engineering to define events, metrics, and measurement frameworks; build and validate dbt models; maintain Looker dashboards; investigate anomalies and tracking issues; and deliver actionable recommendations. The role also involves using AI agents and automation workflows to streamline repeatable analytical and operational processes.

Analyze payment transaction data to monitor approval rates, chargebacks, fraud indicators, and provider performance. Develop dashboards, define payment analytics goals and fraud detection frameworks, investigate anomalies, and recommend data-driven routing and optimization strategies that improve payment performance and reduce losses. Collaborate with Finance, Product, and BI teams while applying SQL, BI, data pipeline, and data warehousing knowledge.

Lead product analytics for digital health products using large healthcare and behavioral datasets. Define metrics, forecast performance, analyze engagement, design A/B tests, apply causal inference and statistical modeling, monitor production ML models, and provide recommendations that shape product strategy. Collaborate with Product, Engineering, Data Science, Data Engineering, Clinical, Marketing, and Customer Success teams while communicating actionable insights through clear data storytelling.

Analyze complex datasets, translate findings into actionable insights, improve data pipelines and client onboarding, create internal and external visualizations, collaborate with clients, contribute to AI model design, and build data analysis infrastructure.

Analyze complex datasets to identify trends, define and implement KPIs, support cross-functional decision-making, and evaluate product feature performance. Translate business and product requirements into analytical logic and clear visualizations using BI tools. Partner with Product, Engineering, Marketing, Sales, Customer Success, Risk, and Data Science teams to deliver actionable insights and promote data-driven growth, with potential focus on compliance, fraud, AML, and financial crime analytics.

Seeking a Data Analyst with strong full-stack development skills in SQL, Node.js, and React. Must have 5 years in data analysis.

Part-time student Data Analyst supporting customer data onboarding, raw-data exploration, parsing and normalization. The role analyzes large-scale real-time connected-vehicle data using big-data and AI tools to identify anomalies, emerging threats, and actionable insights. Responsibilities include developing detection methods, researching complex datasets, and representing the data function in internal and customer-facing interactions.

The Data Analyst will analyze data to drive strategic decisions, build reports and visualizations, and ensure data quality across systems.

Build and administer Tableau dashboards and reports, transform large datasets into actionable insights, optimize queries and ETL processes, collaborate with stakeholders, and support Tableau Server administration and performance monitoring.

Analyze large, complex datasets and design unified data models; research integrations and APIs; modify production logic; support customer onboarding and deliver customer-facing analyses; partner with Engineering, Data Science, and Product to shape Glow's data platform and AI-powered features.

Analyze large financial transaction datasets using SQL and Python (Pandas); investigate data quality and reconciliation issues; build matching and validation processes; develop and maintain BI dashboards; partner with Product, Engineering, and business teams to automate analyses and deliver actionable insights.

Process and validate complex datasets, perform quantitative analysis and data mining, develop tools to ensure data and algorithm quality, evaluate and improve algorithms produced by data scientists, and partner with Big Data Engineers to scale solutions and create new features.

The data analyst will collaborate with the research team, analyze data, provide insights, and enhance datasets for business needs.

The Senior Data Analyst will work with large datasets, collaborate on research methodologies, perform analyses, improve data quality, and communicate findings.

As a Sales Data Analyst, you will analyze and report sales data, provide insights, oversee data collection, maintain databases, and support the sales team.

The Big Data Analyst will analyze large datasets to provide insights, create custom reports, and collaborate with engineers and clients, requiring strong analytical skills and project management experience.

Serve as the analytical partner to Account Managers, diagnose performance issues, surface proactive business intelligence from marketplace data, automate workflows and build tools/models (including recommendation-system related work), and create actionable insights that influence product and business strategy.

Support product team with product-usage analytics, build and maintain modeled data layers and dashboards, run deep-dive analyses to inform product decisions and experiments, measure conversion/retention/churn and unit economics, and translate findings into clear recommendations for non-technical stakeholders.