לוח משרות דרושים הייטק דאטה משרה מלאה, לחיפוש זה נמצאו 200 משרות

We are looking for a seasoned, self-driven professional with a strong sense of ownership who can take a task from start to finish independently.
We also value genuine intellectual curiosity - someone who keeps up with what's happening in the data and AI space, is willing to experiment, and helps the team identify where new technologies can create real value
How will you make an impact?
Design, build, and maintain scalable data pipelines feeding dashboards and analytical products across the R&D organization
Own and manage the cloud data warehouse - schema design, performance tuning, and access control
Manage data ingestion workflows connecting diverse data sources to the central data platform
Develop and maintain transformation models ensuring clean, well-documented, and tested data layers
Accompany the full data lifecycle from raw ingestion to business-ready datasets, and build the corresponding reports and dashboards where needed
Implement data quality checks, monitoring, and alerting to proactively identify and resolve issues
Translate business requirements into data solutions, working across R&D, Product, IT, and Business Operations
Explore emerging AI capabilities as they relate to data engineering and help identify practical opportunities for the team

We are looking for a seasoned, self-driven professional with a strong sense of ownership who can take a task from start to finish independently. We also value genuine intellectual curiosity - someone who keeps up with what's happening in the data and AI space, is willing to experiment, and helps the team identify where new technologies can create real value
How will you make an impact?
Design, build, and maintain scalable data pipelines feeding dashboards and analytical products across the R&D organization
Own and manage the cloud data warehouse - schema design, performance tuning, and access control
Manage data ingestion workflows connecting diverse data sources to the central data platform
Develop and maintain transformation models ensuring clean, well-documented, and tested data layers
Accompany the full data lifecycle from raw ingestion to business-ready datasets, and build the corresponding reports and dashboards where needed
Implement data quality checks, monitoring, and alerting to proactively identify and resolve issues
Translate business requirements into data solutions, working across R&D, Product, IT, and Business Operations
Explore emerging AI capabilities as they relate to data engineering and help identify practical opportunities for the team

we are seeking an experienced Product Owner focused on the Analytics domain of our X‑Sight platform. This role owns the execution and iterative improvement of capabilities to achieve the analytics vision-including risk scoring, detection model development, insights frameworks, and analytical workflows that support Financial Crime and Compliance outcomes.
The Analytics Product Owner will define and prioritize requirements that elevate decisioning accuracy, expand analytical coverage, increase explainability, and improve model and rule operationalization.
You will work closely with Data Science, Engineering, Cloud Ops, Customer Success, and cross‑product stakeholders to deliver reliable, scalable, and innovative analytics capabilities across our multi‑tenant SaaS platform-ensuring they meet the expectations of a modern FCC analytics ecosystem.
How will you make an impact?
Define and refine the roadmap and backlog for the Analytics domain.
Translate market needs and requirements into clear epics, features, and user stories.
Partner with Data Science and Engineering to design and deliver analytics capabilities.
Enhance model lifecycle management, versioning, and monitoring workflows.
Collaborate with Cloud Ops to ensure scalability, cost‑efficiency, and reliability.
Establish KPIs, monitoring signals, and quality checks for analytics reliability.
Work with Support and SMEs to reduce friction and improve analytical clarity.
Contribute to documentation, demos, and enablement for analytics enhancements.
Ensure alignment with multi‑tenant SaaS architecture and governance models.
Collaborate cross‑functionally to deliver new analytic innovations.

We're looking for a Data Platform Engineer to own and scale the Kubernetes infrastructure powering our large-scale data processing platform.

This is a hands-on role at the intersection of infrastructure and data engineering. You'll operate Kubernetes clusters running thousands of nodes, supporting workloads like Spark, Airflow, and remote shuffle services. Your focus: making distributed data workloads reliable, cost-efficient, and performant at scale.

This is not a traditional DevOps or SRE role. You won't be building CI/CD pipelines or managing web services. Instead, you'll be deep in Spark executor scaling, shuffle optimization, batch scheduler tuning, and capacity planning for clusters that process massive datasets daily.

If you've tuned Spark on Kubernetes at scale, wrestled with shuffle storage bottlenecks, or optimized batch scheduling across thousands of concurrent pods - this role is for you.

WHAT YOU'LL DO:
Operate and scale Kubernetes clusters with thousands of nodes supporting large-scale Spark and data processing workloads.
Manage and optimize Apache Spark on Kubernetes - executor autoscaling, driver scheduling, resource tuning, spot instance strategies.
Deploy and tune remote shuffle services (e.g., Apache Celeborn) to handle shuffle data at scale across multiple availability zones.
Operate and improve self-hosted Apache Airflow infrastructure on Kubernetes
Configure and optimize batch schedulers (e.g., YuniKorn, Volcano) for gang scheduling, fair-share queuing, and resource prioritization.
Drive cost optimization across large compute fleets - spot vs. on-demand strategies, node right-sizing, autoscaling policies, local SSD utilization.
Support and collaborate with Data Engineering teams on workload. performance, resource allocation, and infrastructure requirements.
Manage infrastructure-as-code (Terraform) and GitOps deployments (ArgoCD, Helm) for data platform services.
Integrate with managed data platforms (e.g., Databricks) and cloud storage for hybrid processing architectures.

We are looking for a senior Data Scientist to join a team of passionate engineers who build premium solutions at scale. Every day, we tackle ambiguous and stimulating challenges. We build and operate large-scale distributed machine learning systems that process 1B predictions per second during real-time auctions.
What will you do?
As a Senior Data Scientist, your mission will be to:
Improve existing algorithms & tools allowing to explore and analyze more and more data and provide accurate feedback on the Teads activities.
Develop new algorithms & new approaches to provide accurate predictions and drive new products by providing business insights.
Implement your algorithms and models end to end.
Collaborate with a variety of teams to develop services from design to production.
Make sure the software is in good hands by writing, running and automating tests (unit, functional, load...).
Keep up to date with the latest Machine Learning technologies to make sure we always use the best class algorithms according to the context.
Shape how billions of people discover and enjoy premium content by improving ad relevance, quality, and efficiency across Teads global publisher network.

Our main Engineering challenges at Teads
Build efficient and easy-to-use web products used by thousands of users working for the worlds most premium publishers, advertisers, and agencies.
Rich and diverse tech stack and system architecture to optimize for performance, scalability, resiliency, and cost efficiency. We use mostly Scala and TypeScript, among others.
Working in a very high-traffic environment (2.2 billion users per month, 100 billion events per day) with low latency and high availability constraints (2 million requests per second, responses in less than 150 milliseconds).
Management of large datasets with milliseconds order of magnitude access time, to compute in a near real-time complex auction resolution algorithm (18 million predictions per second).
A fast-changing environment where we continuously collaborate with Product teams and constantly adapt our Cloud infrastructure for new features and Products.
Bring a wide diversity of profiles to the same level of quality and knowledge
Work in an international environment with offices located in Israel, Slovenia and France.
Our Core Data Platform team
We're a leading force in the ad tech industry, revolutionizing how brands connect with their audiences. Our platform processes billions of ad impressions daily, generating massive datasets that drive our core business. We thrive on innovation and seek a Data Engineer to help us build and scale the data infrastructure that powers our insights and analytics. This is a unique opportunity to work with cutting-edge technologies and make a direct impact on our products.
What will you do?
As a Data Engineer, you'll be a key part of our data platform team, responsible for designing, building, and maintaining robust and scalable data pipelines. You'll work closely with data scientists, analysts, and server side engineers to ensure our data is reliable, accessible, and ready for analysis. Your expertise will be crucial in expanding our data warehouse and data lake capabilities, enabling us to deliver next-generation ad tech solutions.
Your mission will be to:
Develop and Optimize Data Pipelines: Design, build, and maintain ETL/ELT pipelines using Apache Spark to ingest, process, and transform large-scale datasets from various sources.
Manage Cloud Infrastructure: Architect and manage our data infrastructure primarily on Google Cloud Platform (GCP) or Amazon Web Services (AWS). This includes services like BigQuery, S3, GCS, EMR, and AirFlow.
Enhance Data Storage: Improve and manage our data warehouse and data lake solutions, ensuring data quality, consistency, and accessibility for business intelligence and machine learning applications.

Our main Engineering challenges at our company
Build efficient and easy-to-use web products used by thousands of users working for the worlds most premium publishers, advertisers, and agencies.
Rich and diverse tech stack and system architecture to optimize for performance, scalability, resiliency, and cost efficiency. We use mostly Scala and TypeScript, among others.
Working in a very high-traffic environment (2.2 billion users per month, 100 billion events per day) with low latency and high availability constraints (2 million requests per second, responses in less than 150 milliseconds).
Management of large datasets with milliseconds order of magnitude access time, to compute in a near real-time complex auction resolution algorithm (18 million predictions per second).
A fast-changing environment where we continuously collaborate with Product teams and constantly adapt our Cloud infrastructure for new features and Products.
Bring a wide diversity of profiles to the same level of quality and knowledge
Work in an international environment with offices located in Israel, Slovenia and France.
About the opportunity
We are looking for a Senior Data Scientist to join a team of applied research.
The AI department currently consists of 50 people who are a mix of data scientists, machine learning and backend engineers.
The department provides technologies that power outcomes of campaigns with a total yearly turn over of $1.7B.
Runs large scale prediction and control systems for ad delivery, dealing with millions of live ads, doing more than a billion predictions per second based on large on-line trained models being updated every 5 minutes.
What will you do?
As a Senior Data Scientist, your mission will be to:
Innovate at Scale: Stay at the forefront of AdTech research, focusing on CTR prediction, online learning regimes, and high-dimensional embeddings.
Bridge Research & Engineering: Collaborate with cross-functional teams to identify research gaps and translate them into production-ready features.
Own the Lifecycle: Lead the end-to-end process-from reading the latest ArXiv papers to A/B testing and monitoring model performance in a live environment.
Experiment Freely: Dedicate time to "deep-work" sessions, testing unconventional hypotheses and new algorithmic approaches.

We're a leading force in the ad tech industry, revolutionizing how brands connect with their audiences. Our platform processes billions of ad impressions daily, generating massive datasets that drive our core business. We thrive on innovation and seek a Data Engineer to help us build and scale the data infrastructure that powers our insights and analytics. This is a unique opportunity to work with cutting-edge technologies and make a direct impact on our products.
What will you do?
As a Big Data Engineer, you'll be a key part of our data platform team, responsible for designing, building, and maintaining robust and scalable data pipelines. You'll work closely with data scientists, analysts, and server side engineers to ensure our data is reliable, accessible, and ready for analysis. Your expertise will be crucial in expanding our data warehouse and data lake capabilities, enabling us to deliver next-generation ad tech solutions.
Your mission will be to:
Develop and Optimize Data Pipelines: Design, build, and maintain ETL/ELT pipelines using Apache Spark to ingest, process, and transform large-scale datasets from various sources.
Manage Cloud Infrastructure: Architect and manage our data infrastructure primarily on Google Cloud Platform (GCP) or Amazon Web Services (AWS). This includes services like BigQuery, S3, GCS, EMR, and AirFlow.
Enhance Data Storage: Improve and manage our data warehouse and data lake solutions, ensuring data quality, consistency, and accessibility for business intelligence and machine learning applications.
Collaborate and Innovate: Partner with cross-functional teams to understand data needs and implement solutions that support new product features and business initiatives.
Ensure Data Integrity: Implement monitoring, alerting, and logging systems to maintain data pipeline health and ensure data accuracy.

We are looking for an experienced and passionate Data Engineer to join the Data Engineering Team in our rapidly growing TLV R&D site!
You will be instrumental in maintaining current pipelines and expanding our data semantic layer to support both traditional analytics and our future AI/ML initiatives.
Responsibilities include working alongside developers from the BI and Backend teams, architects and business decision makers in order to implement data pipelines and improve data architecture and infrastructure.
The Data Engineering Team focuses on building long term, scalable self-service solutions for the organizational growing data needs.
What You'll Do:
Design & Build Robust Pipelines: Develop, deploy, and maintain scalable, highly reliable, and idempotent ELT data pipelines using Python and orchestration tools like Airflow.
Own the Data Model: Lead data transformation and modeling efforts within our cloud data warehouse (e.g., Snowflake, AWS) using dbt, ensuring adherence to modern analytics engineering best practices (modularity, DRY principles, and clear separation of staging and data marts).
Expand the Semantic Layer: Architect and grow our centralized semantic layer to establish a "single source of truth" for business metrics, powering both traditional BI dashboards and upcoming AI initiatives.
Champion Data Quality & Reliability: Implement rigorous data validation, testing, and monitoring to ensure data integrity and build trust with downstream consumers.
Enable Self-Service Analytics: Design intuitive, long-term data infrastructure solutions that empower business stakeholders, analysts, and developers to easily and independently query organizational data.
Cross-Functional Collaboration: Partner closely with Backend developers, BI analysts, architects, and business decision-makers to translate complex business requirements into efficient technical architectures.

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

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from our office.

We are looking for a talented Data Engineer to help build and enhance the data platform that supports analytics, operations, and data-driven decision-making across the organization. You will work hands-on to develop scalable data pipelines, improve data models, ensure data quality, and contribute to the continuous evolution of our modern data ecosystem.

Youll collaborate closely with Senior Engineers, Analysts, Data Scientists, and stakeholders across the business to deliver reliable, well-structured, and well-governed data solutions.


What Youll Do
Engineering & Delivery
Build, maintain, and optimize data pipelines for batch and streaming workloads.

Develop reliable data models and transformations to support analytics, reporting, and operational use cases.

Integrate new data sources, APIs, and event streams into the platform.

Implement data quality checks, testing, documentation, and monitoring.

Write clean, performant SQL and Python code.

Contribute to improving performance, scalability, and cost-efficiency across the data platform.

Collaboration & Teamwork

Work closely with senior engineers to implement architectural patterns and best practices.

Collaborate with analysts and data scientists to translate requirements into technical solutions.

Participate in code reviews, design discussions, and continuous improvement initiatives.

Help maintain clear documentation of data flows, models, and processes.

Platform & Process

Support the adoption and roll-out of new data tools, standards, and workflows.

Contribute to DataOps processes such as CI/CD, testing, and automation.

Assist in monitoring pipeline health and resolving data-related issues.

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from our office.

We are looking for a highly skilled Senior Data Engineer with strong architectural expertise to design and evolve our next-generation data platform. You will define the technical vision, build scalable and reliable data systems, and guide the long-term architecture that powers analytics, operational decision-making, and data-driven products across the organization.

This role is both strategic and hands-on. You will evaluate modern data technologies, define engineering best practices, and lead the implementation of robust, high-performance data solutions-including the design, build, and lifecycle management of data pipelines that support batch, streaming, and near-real-time workloads.

What Youll Do

Architecture & Strategy
Own the architecture of our data platform, ensuring scalability, performance, reliability, and security.
Define standards and best practices for data modeling, transformation, orchestration, governance, and lifecycle management.
Evaluate and integrate modern data technologies and frameworks that align with our long-term platform strategy.
Collaborate with engineering and product leadership to shape the technical roadmap.

Engineering & Delivery
Design, build, and manage scalable, resilient data pipelines for batch, streaming, and event-driven workloads.
Develop clean, high-quality data models and schemas to support analytics, BI, operational systems, and ML workflows.
Implement data quality, lineage, observability, and automated testing frameworks.
Build ingestion patterns for APIs, event streams, files, and third-party data sources.
Optimize compute, storage, and transformation layers for performance and cost efficiency.

Leadership & Collaboration
Serve as a senior technical leader and mentor within the data engineering team.
Lead architecture reviews, design discussions, and cross-team engineering initiatives.
Work closely with analysts, data scientists, software engineers, and product owners to define and deliver data solutions.
Communicate architectural decisions and trade-offs to technical and non-technical stakeholders.

We are looking for a strategic product leader to head Data Platform domain - a core area of our offering. This role is critical to the companys growth, driving both short-term impact and long-term vision for managing and leveraging massive amounts of structured and unstructured clinical data. In this role, you will lead the product strategy and execution for the domain, while guiding and mentoring product managers within the team. You will work closely with Engineering, Data Science, Clinical Experts, the rest of the Product group, as well as partners and strategic customers, shaping how our platform operates and scales to meet the needs of our growing business.
Responsibilities:
Define and drive the roadmap for Data Platform, balancing short-term business needs with long-term scalability and platform strategy.
Own the product vision for data ingestion, processing, and access, ensuring the platform supports all product groups and business initiatives.
Lead and mentor a team of product managers, fostering growth, collaboration, and a culture of ownership and impact.
Support your team in translating complex technical and data requirements into clear product specifications, prioritizing initiatives that maximize business impact.
Partner closely with Engineering, Data Science, Clinical Experts, Customer Success, the entire PM group, and external partners and strategic clients, to design and prioritize initiatives, ensuring data quality, performance, and usability.
Drive cross-team technical initiatives end-to-end, coordinating dependencies and aligning stakeholders across the organization.
Establish key metrics to measure platform performance, adoption, and impact on product outcomes.