לוח משרות דרושים הייטק דאטה משרה מלאה, לחיפוש זה נמצאו 188 משרות

Required Senior Data Engineer
21679 ID
Role Description:
As a Senior Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.
Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.
Key Job Responsibilities and Duties:
Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.
Dealing with massive textual sources to train GenAI foundation models.
Solving issues with data and data pipelines, prioritizing based on customer impact.
End-to-end ownership of data quality in our core datasets and data pipelines.
Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.
Providing tools that improve Data Quality company-wide, specifically for ML scientists.
Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.
Acting as an intermediary for problems, with both technical and non-technical audiences.
Promote and drive impactful and innovative engineering solutions
Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

As a AI-Data Engineer, youll take a leading role in building the foundation of our companys Agentic Exposure Management platform: the source of truth that tells a security team what they have, whats actually risky and why, how to fix it, and who is responsible.
Enterprises run their security across a sprawl of cloud and on-prem tools. Making sense of that sprawl is the heart of what we do. Were building a complex data fabric that reconciles data from a hundred different sources, applies sophisticated transformation and enrichment, and turns partial, uncertain signal into one trustworthy model of an organizations real exposure.
Key Responsibilities:
Model the security graph. Design an ontology that spans both the modern cloud and the classical on-prem world, abstract enough to absorb 100+ different integrations under one model, yet rich enough to carry everything needed for decision making. Decide whats an entity, whats an edge, and whats a denormalized attribute, across millions of assets and finding rows.
Own asset identity engineering and reconciliation. Upstream identifiers are uncertain and volatile. Engineer deterministic identity resolution for assets and findings arriving from different vendors.
Build LLM-based and agentic pipelines for data classification, entity extraction, and enrichment, optimizing for cost, latency, and precision in production.
Build the integration factory. Stand up a scalable integration platform that drives down time-to-integration.
Build and maintain complex ETL pipelines handling millions of records: incremental sync, complex correlation, and data quality observability and monitoring.

We are seeking an experienced Data Science Team Lead to lead our data science and data engineering efforts and oversee a team of skilled data scientists & engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.
The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions - like demand forecasts - to drive revenue management decisions that actually move the needle.
At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and running it through a price optimization decision model that simulates the market - weighing competition, pricing constraints, inventory availability, predictive models, and each client's unique business policies - to generate the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything.
You will lead the team through architecture decisions, development, and deployment of mission-critical systems-while growing and mentoring a high-performing team.
Responsibilities:
Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
Analyzing existing models and algorithms, researching for new approaches in price optimization.
Design, build and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve our data architecture in alignment with business and product goals.
Manage sprint planning, task breakdown, code reviews, and performance feedback for your team.
Contribute hands-on to key development tasks and architecture decisions.
Recruit, mentor, and grow the data science engineering team.

The Large Market Model group (LMM) is looking for a Director of Data Science to lead its production engineering, overseeing MLOps, ML engineering and Data engineering. The Director owns the end to end ML lifecycle of the market model, from the data pipelines that feed it, through model training and serving, and on to the monitoring that keeps it healthy once it is live in customer systems.
Crucially, the Director is responsible for setting the roadmap across these functions, alongside LMM management and senior executives, and for turning it into work each function can execute. We are looking for a manager who has led high paced, AI first groups, and who has carried deliveries for systems running live in front of customers.
RESPONSIBILITIES
Decide priorities across the functions with their leads, and review the technical trade-offs yourself.
Manage the leads who run these functions and hold each of them to what their team has committed to.
Stay close to production. Own delivery dates, reliability, and what happens when something breaks.
Own the infrastructure for model training, serving, and large scale data processing, including its cost.
Decide which numbers (KPIs) the group is measured on, then track them and report them.
Report the group's progress and risks to senior management, to product, and to the commercial teams.
Work with the applied research team to move their results into production.
Hire and keep senior engineers, and decide how the group is staffed.
Set the engineering standards the group works to: CI/CD, automated testing, observability, and model monitoring.

We are seeking a skilled and motivated Data Engineer with expertise in Elasticsearch, cloud technologies, and Kafka. As a data engineer, you will be responsible for designing, building and maintaining scalable and efficient data pipelines that will support our organization's data processing needs.
The role will entail:
Design and develop data platforms based on Elasticsearch, Databricks, and Kafka
Build and maintain data pipelines that are efficient, reliable and scalable
Collaborate with cross-functional teams to identify data requirements and design solutions that meet those requirements
Write efficient and optimized code that can handle large volumes of data
Implement data quality checks to ensure accuracy and completeness of the data
Troubleshoot and resolve data pipeline issues in a timely manner.

We are seeking an experienced Technical Team Lead to head the development of a new Cyber Platform. This is a hybrid, 50/50 role, splitting your time evenly between people management and core system architecture & development. You will lead a versatile engineering team composed mostly of Data Engineers, alongside Backend and Frontend developers.
In this role, you will be responsible for a distributed microservices architecture, complex cyber-data fusion, and large-scale data pipelines under strict SLA constraints, operating in both public Cloud and highly secure, closed On-Premises environments.
Responsibilities:
Leadership & Code Quality: Directly lead, mentor, and manage the team while actively conducting Code Reviews (CRs) and setting high engineering standards.
Hands-on Development: Drive core system development (at least 50% hands-on) with a strong focus on Python.
Architecture & Data Pipelines: Lead the design and architecture of distributed systems, high-performance data pipelines, and data-fusion algorithms.
Environment Management: Build and deploy scalable solutions across both Public Cloud and secure, closed On-Prem environments.
Optimization & SLA: Ensure data integrity and optimize complex bulk queries to meet strict response-time targets.

As a Senior Research Scientist, AI & Data Systems at Eon Research, you will conduct applied research at the intersection of AI, data systems, and large-scale storage. you will explore challenging technical problems, publish research in leading peer-reviewed venues, file patents, and translate research breakthroughs into production-ready product capabilities.

This role is ideal for a researcher with a strong publication record, deep expertise in a relevant research area, and experience bringing research into real-world products.

Research Areas
Query Processing and Query Engines: Query optimization and execution over columnar and lakehouse formats, including Apache Iceberg and Parquet, as well as approximate query processing using sketches and synopses.

Storage Systems: Deduplication, compression, and data layout optimization for large-scale storage.

Data Integration and Discovery: Entity resolution, schema matching, table joinability, and data discovery, including the use of LLMs as judges.

Information Retrieval and RAG: Retrieval-augmented generation over enterprise data, including emails, documents, and databases.

LLM Evaluation and Benchmarking: Evaluation frameworks and benchmarks for LLM agents working with enterprise data.

LLMs for Software Engineering: CI failure diagnosis, automated debugging, and program repair.

We are looking for a mid-level Data Engineer, experienced with building data pipelines using Python and Airflow for structured data and relational databases. The ideal candidate is a self-directed learner, takes ownership of the pipelines they build, and cares about data quality as much as feature delivery.





Key Responsibilities:

Own our data pipelines, including maintenance and adding new features.
Take charge of data integrity and completeness, including writing and maintaining tests.
Improve process efficiency and optimize runtime performance.
Own schema design and evolution across our data pipelines.

we are looking for a Data Operations Team Lead.
The Data Engagement team is the connective link between GTM and R&D/Product organizations. The team leads customer and prospect data initiatives using internal tools, and drives product improvements based on patterns and issues analysts identify in their day-to-day work.
What you'll do:
Own execution of the team's initiatives - ensuring efforts are properly resourced, tracked, and delivered within required timeframes.
Play a key role in prioritizing data resources across company-wide efforts, working closely with stakeholders across R&D, Product, Customer Success, and Sales.
Lead and develop a multidisciplinary team of highly skilled analysts.
Build strong partnerships with Product and CS leaders, acting as their trusted data advisor to help achieve shared business objectives.

As a Senior Data Scientist , you will be an integral part of our Machine Learning and AI team. The role is hands-on and impact-driven, focusing on building, improving, and delivering machine learning models that directly support business goals.

Responsibilities
Design and deploy production ML systems that drive automated business decisions (pricing, bidding, resource allocation)
Build end-to-end ML pipelines - from data ingestion and model training to serving, monitoring, and incident response
Translate ambiguous business problems into rigorous mathematical frameworks and own them from conception to production impact
Conduct rigorous experimentation (A/B testing, causal inference, uplift modeling) to measure and improve model performance
Maintain and improve real-time models that adapt to incoming data and feedback signals
Mentor junior team members on ML best practices and production standards

As a hands-on individual contributor, you will act as the primary technical expert for the marketing department when data discrepancies or tracking failures occur (e.g., missing conversions, Meta Ads signal drops, or AppsFlyer event mismatches). This position requires a proactive professional who uses SQL and data warehouse environments to identify and resolve anomalies before they impact reporting.

This is a dedicated technical specialist role focused entirely on deep ownership of data infrastructure and troubleshooting, without people-management responsibilities.

Responsibilities
Production Support & Troubleshooting

Own the end-to-end resolution of tracking and signal-delivery incidents (detection, root cause analysis, remediation, and documentation).
Diagnose data discrepancies across three distinct delivery paths: client-side GTM tags, Server-to-Platform CAPI, and reverse ETL.
Address and resolve brand- or country-specific tracking constraints, including regulatory and consent requirements.
Data Quality & Analysis

Conduct regular audits of event and conversion data across major platforms (Google Ads, Meta Ads, GA4, AppsFlyer, TikTok, and programmatic partners) to prevent attribution distortions.
Utilize SQL within Snowflake to validate that events sent via CAPI and reverse ETL match system specifications.
Document technical incidents and resolutions in clear, actionable reports for both technical and non-technical stakeholders.
AppsFlyer & GTM Ownership

Manage configurations and serve as the primary subject-matter expert for AppsFlyer and Google Tag Manager (both client-side and server-side) across all brands.
Partner with the internal GA4 team on cross-functional data issues (without holding direct responsibility for GA4 configuration).

a Senior Data Analyst position to join the BI Team, with a passion for technology, to solve unique problems within our measurements.
Responsibilities
Function as the data authority in the Monetization domain - performing ad-hoc research and analysis on the company's rich data to support unique and strategic business questions.
Define, analyze, and monitor KPIs and OKR metrics to continually improve company products.
Analyze large, complex data sets to address strategic and operational business questions.
Actively take part in building and maintaining our AI Driven-BI Agentic Architecture.
Build dashboards and visualizations to answer key questions for the company's in-house various teams, specifically for the Monetization Team.
Perform qualitative, quantitative, and statistical analysis.
True passion for understanding data and finding Insights.
Provide actionable recommendations and conclusions from data to shape features and future product road maps.