לוח משרות דרושים הייטק דאטה, לחיפוש זה נמצאו 214 משרות

abra Microsoft Division is seeking a Data Solutions Lead We are looking for an experienced Data Solutions Lead to drive data and AI solutions through architectural consulting, POCs, and hands-on delivery. This role combines technical leadership, presales activities, tool selection, and direct work with customers on complex data projects. Key Responsibilities:
* Lead architectural consulting and recommend data solution designs.
* Build POCs and stay hands-on with Python, SQL, and data platforms.
* Support presales processes, tool selection, and technical decision-making.
* Lead complex data projects and communicate directly with customers.

we are looking for a Senior Data Engineer to join our Data Platform team. We are seeking a hands-on individual with experience in building and scaling modern data infrastructure, passionate about exploring AI technologies and applying them to enhance and evolve our data systems. This role is ideal for someone focused on data systems, performance, automation, and reliability, who will design, build, and maintain core components of our data pipelines, storage, monitoring, and observability stack, with a strong focus on improving workflows and system capabilities, while embracing the Shift4 way.

Responsibilities:

Build and maintain scalable, reliable, and high-performance data infrastructure across cloud and on-prem environments
Partner with engineering, analytics, and observability teams to support data flow from ingestion to consumption
Develop infrastructure as code and automation tools to improve deployment, monitoring, and recovery
Help troubleshoot performance bottlenecks and reliability issues in large-scale data pipelines
Implement data quality processes aligned with security standards to ensure accuracy, consistency, and compliance with financial regulations and PCI requirements.
Continuously improve the performance and efficiency of the data platform with proactive tuning and benchmarking
Proactively explore and apply AI tools to enhance efficiency across the data engineering lifecycle, while improving accessibility and usability of data solutions for teams across the organization

We are expanding, seeking an experienced Data Engineer for challenging work in the world of Big Data.
Design, develop & maintain the BIG DATA platform (SingleStore/Vertica).
Design, develop & maintain the BIG DATA infrastructure (development, monitoring, installation, integration, etc)
Be part of the dynamic and agile core DBA team.
Collaborate with field engineers for the deployment phases.
Work on major, challenging projects with enterprise and startup customers.
Job location - Herzliya, Israel.

Required Senior Finops Analyst
Job responsibilities
What will you do?
The Senior FinOps Analyst will ensure financial requirements are clearly defined and translated into effective system solutions. This role focuses on gathering, structuring, and documenting detailed requirements for financial systems, driving alignment between Finance and Technology teams.
Key Responsibilities:
Requirement Gathering & Documentation: Work closely with Finance teams to understand their needs, pain points, and objectives. Translate business needs into structured, detailed, and well-documented requirements for Technology teams.
Financial Systems Enhancements: Partner with MIS and BI teams to ensure financial systems meet evolving business needs. Define system improvements and validate solutions before implementation.
Cross-Team Collaboration: Act as the primary link between Finance, MIS, BI, PMO, and other relevant teams, ensuring smooth communication and alignment of priorities.
Process Optimization: Identify inefficiencies in financial workflows and propose system-driven solutions that enhance automation and accuracy.
Strategic Initiatives Support: Contribute to large-scale financial system projects by providing clear, structured requirements and ensuring business objectives are met.

Lead the team building the agentic infrastructure powering Klaudia - our AI SRE. You'll own the architecture, set the technical direction, and push experimental ideas into production.

This isn't a coordination role. You'll be a technical leader who builds alongside your team - setting the bar for architecture quality, driving experimental bets, and turning research into production-grade systems.



What will you do?

Own the agentic infrastructure- You're responsible for the architecture of Klaudia's core - the systems that make autonomous investigation, root cause analysis, and self-healing possible at scale.

Build and grow your team- Hire great engineers, mentor them, run meaningful design reviews, and build a culture of high ownership and engineering excellence.

Drive experimental development- Run structured bets - LLM evaluation frameworks, new agent architectures, novel tool-use patterns - and bring the best ones to production.

Stay hands-on You'll design systems, review PRs critically, and write code where it matters. Your technical credibility is the foundation of your leadership.

Shape our AI strategy- Work closely with product and R&D leadership to define what Klaudia becomes next.



Why this role rocks?

Category-defining work- AI SRE is the hottest space in cloud infra. You'll be leading the team that defines what it means - not catching up to anyone.

Real autonomy- You'll set the technical roadmap for Core. No pre-solved problems handed down to you - you decide what gets built and how.

Build something that lasts- Hire and shape a team from an early stage. Your engineering culture decisions here will compound for years.

Serious engineering culture- High bar for craft, strong team around you, and leadership that respects technical depth over process theater.

we are looking for a Data science Team Lead.
As the Data Science Team Lead, you will lead a talented team of data scientists and ML engineers building the infrastructure, systems, and workflows for designing, training, evaluating, and deploying machine learning models that protect millions of users worldwide from fraud and account compromise.
This role combines hands-on technical leadership with people management and strategic ownership. You will drive innovation across real-time model serving, customer-specific model tuning, offline AI evaluations, and scalable ML systems in a production-grade SaaS environment.
If you are passionate about applied machine learning, fraud detection, and building intelligent systems at scale - we want you on our team.
What youl do:
Lead and mentor a team of Data Scientists and ML Engineers focused on fraud detection and response capabilities.
Build ML infrastructure focused on design, train, evaluate, and optimize machine learning models for real-time fraud prevention and risk assessment.
Own the lifecycle of ML models in production, including experimentation, deployment, monitoring, retraining, and performance optimization.
Drive customer-specific model training and tuning strategies to improve accuracy and adaptability across different customer environments.
Build and improve offline AI evaluation frameworks to measure model quality, drift, effectiveness, and business impact.
Collaborate closely with Engineering, Product, Security, and Data teams to deliver scalable and reliable AI-powered capabilities.
Define best practices for model serving, feature engineering, experimentation, observability, and operational excellence.
Balance model performance, latency, scalability, explainability, and operational constraints in high-scale production environments.
Promote a culture of technical excellence, continuous improvement, ownership, and innovation.

Were looking for a Senior Data & Analytics Architect to join our team and work directly with startup CTOs, engineering teams, and account teams. This is a hands-on, customer-facing architecture role where you will design and deliver production-grade data platforms on AWS.

The role is focused on turning real business problems into working systems, from early discovery and architecture design to supporting implementation. This is not a pure strategy or management role, its about making clear technical decisions and ensuring they hold up in production.

Work location: hybrid from Tel Aviv.

If you are interested in this opportunity, please submit your CV in English.

Key Responsibilities:
Design and deliver end-to-end data platforms on AWS, covering ingestion, storage, processing, and serving layers, including Well-Architected reviews and contribution to SOWs and HLDs.
Work directly with startup customers to understand requirements, challenge assumptions, and shape practical solutions.
Lead technical discussions, workshops, architecture reviews, and POCs.
Translate ideas and early concepts into clear architectures, including HLDs, SOWs, and implementation direction.
Make and explain trade-offs across cost, performance, scalability, and complexity.
Stay close to delivery, supporting teams to ensure solutions are implemented correctly.
Collaborate with Data Engineers, DevOps, Solution Architects and Account Managers to move projects from concept to production.
Contribute to internal standards, reusable patterns, and reference architectures.
Operate in a fast-paced, project-based environment with multiple customer engagements.
Stay up to date with modern Data Engineering and Analytics practices, and contribute to internal enablement, patterns, and knowledge sharing.
Work in a customer-facing environment that includes occasional travel, both locally for meetings and events, and internationally around once per quarter.

Were looking for a seasoned R&D Team Leader to join our top-notch technology R&D team, reporting directly to the VP R&D. In this role, you will drive technological innovation, oversee the development of cutting-edge features, and guide the team in delivering scalable, high-quality products. You will combine hands-on technical leadership with strategic oversight, ensuring that our platform continues to evolve and deliver impactful solutions for our clients. As part of an AI-first company, our web team is deeply involved in creating and integrating AI-driven features to enhance our digital offerings.



Key Responsibilities:

Lead the R&D team in developing new features and enhancing our platform.
Oversee the end-to-end development lifecycle, from ideation to deployment.
Ensure high-quality code, scalable architecture, and smooth product delivery by implementing best practices in software development.
Mentor and support team members in their professional growth.
Troubleshoot and resolve complex technical challenges in collaboration with the team.
Collaborate with cross-functional teams including Product, Data, and AI to align development efforts with company goals.
Drive innovation and stay updated on new technologies to keep us at the forefront of the industry.

we are growing fast, and as we grow we scale both in the number of patients we treat and in the number of products we have in our portfolio. Your mission is to design and build the next-generation data infrastructure and pipelines, in addition contributing to an ongoing project that serves both our customers and internal data teams with scalable and reliable solutions.

You will:
Make a critical contribution to shaping and developing the core of our Data Platform used for AI research and business intelligence, from design to implementation.

Design and build complex data pipelines to move and manipulate data from a wide variety of data sources.

Create a system that will be used to support our data product offering.

Closely collaborate with various teams, including Software Engineering, AI Engineering and Product Management .
Tech stack:

We are running on AWS with Python as the main programming language for the backend. We are using various technologies such as S3, DBT, ETL, ELT, Spark, DynamoDB, Postgres, RabbitMQ, Docker, Redis, Prometheus, Terraform and others. We are always open to new technologies and tools that will help us to do our job even better.

What success looks like:
Self-contributor who excels at working independently and can also collaborate effectively as a valuable team member.

Mentor and advise other team members in code review, performance, scalability and more.

Eager to learn new things and also proactively suggest new ideas and initiatives.

Excellent communication and collaboration skills.

Team player, highly motivated, can-do approach.

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.