לוח משרות דרושים מהנדס/ת דאטה/DATA ENGINEER בתל אביב יפו, לחיפוש זה נמצאו 62 משרות

We are looking for a Data Analytics Engineer with strong technical skills to join the Marketing Data team. You will be the go-to person for how Marketing and other teams measure what matters. You will define the metrics, build the analysis behind them, and keep the data underneath them reliable.

Our marketing data model on BigQuery is already in production. You will report to the VP Growth and work directly with Marketing, Product and Sales leaders.



What You'll Do
Work with Revenue leaders to turn business questions into clear metric definitions, KPIs and measurement plans.
Build analysis and dashboards on acquisition, conversion, activation, feature adoption and retention.
Identify gaps in tracking and work with R&D to close them.
Maintain and extend existing pipelines and models (Airflow, Fivetran, ad platform connectors, BigQuery transformations).
Monitor data quality and fix issues before they reach stakeholders.
Document metric definitions, models and decisions as you go.

We are looking for a hands-on Data Lead to build and own our data domain end-to-end.
This is a key role for someone who combines strong engineering capabilities, deep data passion, product thinking, and business understanding. We are looking for a builder who can independently create the data infrastructure, deliver reliable dashboards and insights, support product and business decision-making, and turn data into a strategic asset for the company.
This is not just a reporting or BI role. You will be responsible for building the data foundation that will support our next stage of growth- across infrastructure, product analytics, business insights, executive reporting, customer-facing analytics, and future AI-driven use cases.
What youll do:
Own our data domain end-to-end- from infrastructure and pipelines to dashboards, insights, and business impact.
Build and maintain scalable data pipelines across product, finance, GTM, customer, and operational data sources.
Design reliable data models and create a clear source of truth for key company metrics.
Build business and executive dashboards that help leadership understand the state of the business.
Analyze product usage, engagement, adoption, retention, feature performance, and customer value.
Support Product, R&D, Sales, Marketing, Customer Success, Finance, and leadership with meaningful data insights.
Translate business and product questions into clear data analysis and actionable recommendations.
Work closely with R&D, while being technically independent and able to build and deliver solutions without relying on engineering support for every step.
Define data standards, documentation, quality checks, metric definitions, and best practices.
Use modern AI tools to accelerate data development, analysis, documentation, automation, and insight generation.
Identify opportunities where our data can support smarter product experiences, automated insights, customer-facing analytics, and AI-supported decision-making.

We are looking for an experienced DevOps Data Infra Engineer to join our growing team. As a Data Infra Engineer, you will play a critical role in setting up, optimizing, and maintaining our data & analytics infrastructure. You will work closely with the development and Security teams to ensure the reliability, scalability, redundancy, and security of our data infrastructure platform.

The ideal candidate is passionate about big data, loves working with cutting-edge technologies, and thrives in a collaborative environment.

What you will do:

Design, implement, and manage scalable, highly available databases for both transactional, real-time use as well as backend analytics and big data.

Build, design, and maintain data processing pipeline infrastructure used by our various applications

Develop and maintain CI/CD processes for automating seamless changes to data infrastructure, including data migrations and database upgrades

Work closely with developers to make sure our data infrastructure meets existing and new feature requirements

Work alongside our Security Team to ensure our infrastructure meets rigorous security standards

Ensure the data infrastructure processes adhere to best practices in security and compliance.

We are seeking talented and passionate Senior Data Engineer to join our Data team. In this pivotal role, you will be instrumental in designing, building, and optimizing the critical data infrastructure that underpins innovative creative intelligence platform. You will tackle complex data challenges, ensuring our systems are robust, scalable, and capable of delivering high-quality data to power our advanced AI models, customer-facing analytics, and internal business intelligence. This is an opportunity to make a significant impact on our product, contribute to a data-driven culture, and help solve fascinating problems at the intersection of data, AI, and marketing technology.
Key Responsibilities:
Architect & Develop Data Pipelines: Design, implement, and maintain sophisticated, end-to-end data pipelines for ingesting, processing, validating, and transforming large-scale, diverse datasets.
Manage Data Orchestration: Implement and manage robust workflow orchestration for complex, multi-step data processes, ensuring reliability and visibility.
Advanced Data Transformation & Modeling: Develop and optimize complex data transformations using advanced SQL and other data manipulation techniques. Contribute to the design and implementation of effective data models for analytical and operational use.
Ensure Data Quality & Platform Reliability: Establish and improve processes for data quality assurance, monitoring, alerting, and performance optimization across the data platform. Proactively identify and resolve data integrity and pipeline issues.
Cross-Functional Collaboration: Partner closely with AI engineers, product managers, developers, customer success and other stakeholders to understand data needs, integrate data solutions, and deliver features that provide exceptional value.
Drive Data Platform Excellence: Contribute to the evolution of our data architecture, champion best practices in data engineering (e.g., DataOps principles), and evaluate emerging technologies to enhance platform capabilities, stability, and cost-effectiveness.
Foster a Culture of Learning & Impact: Actively share knowledge, contribute to team growth, and maintain a strong focus on how data engineering efforts translate into tangible product and business outcomes.

Were hiring a hands-on Senior Data Engineer who wants to build data products that move the needle in the physical world. Your work will help construction professionals make better, data-backed decisions every day.
Responsibilities:
Lead the design and development of scalable data pipelines.
Collaborate with Product, Data Science, and Analytics teams.
Build and optimize workflows using Databricks, Spark, Kafka, and AWS.
Implement real-time and batch processing architectures.
Develop and maintain ingestion pipelines from multiple sources.
Manage deployment and performance of data infrastructure.
Use Terraform to manage infrastructure-as-code.
Prepare data for analytics and product-facing use cases.

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 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.

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.

About The Position
Design, build, and maintain scalable data pipelines and infrastructure entirely within GCP
Own data solutions end-to-end, from architecture and design through to production delivery
Build and maintain custom connectors to advertising platforms including Google Ads, Meta (Facebook) Marketing API, and other ad platforms
Design and implement data warehouse structures with a deep focus on performance, scalability and usability
Continuously investigate and evaluate new GCP native tools and services, assessing their fit, building proof of concepts, and driving adoption where it makes sense
Set up and configure orchestration and transformation frameworks (such as Airflow and dbt) within the GCP environment
Collaborate closely with backend engineers to ensure data infrastructure aligns with product and system needs
Contribute to data science initiatives and bring genuine curiosity to analytical use cases
Monitor, troubleshoot, and continuously improve the reliability and performance of existing data systems
Define technical standards and best practices for the data platform
Build and maintain data dashboards and reports to surface insights for stakeholders, primarily using Looker Studio.

We are looking for an experienced Senior Data Engineer to join our Data team. In this role, you will lead and strengthen our Data Team, drive innovation, and ensure the robustness of our data/analytics platforms.
A day in the life and how youll make an impact:
Design and develop high-performance Data Pipelines and ETL processes to support diverse business needs.
Work closely with Product, Business Intelligence, Sales and other teams to integrate data solutions, ensuring seamless alignment and collaboration across functions.
Continuously improve our data analytics platforms, optimizing system performance and ensuring robust, reliable data infrastructure.
Oversee the entire data lifecycle, from infrastructure setup and data acquisition to detailed analysis and automated reporting, driving business growth through data-driven insights.
Implement robust data quality checks, monitoring mechanisms, and data governance policies to maintain data integrity and security, troubleshooting and resolving any data-related issues efficiently.

We are looking for an Engineering Director to join our engineering leadership team, reporting directly to the VP of Engineering.
In this leadership role, you will own & manage a multi-disciplinary Engineering group spanning AIgo, full-stack, AIOps, QA, and data - accountable for delivery, people, and business outcomes. You will work closely with a Principal Product Manager who sets direction, and directly manage a Product Owner embedded in your team.
Heres what youll be doing:
Own your Engineering group delivery end-to-end - planning, prioritization, dependencies, risks, and outcomes.
Drive accountability: commitments are kept, blockers surfaced early, quality is non-negotiable.
Directly manage all engineers and the Product Owner in your squad, supported by functional guilds.
Run 1:1s, give meaningful feedback, support growth, and build a high-accountability team culture.
Provide technical guidance in close partnership with Principal Architects and senior engineers.
Stay engaged enough to earn trust, spot risk early, and make informed trade-off calls.
Connect your squads work to business outcomes. Always ask why it matters before deciding what to build.
Participate actively in leadership discussions on roadmap, resourcing, and strategy.

We are looking for a Senior Data & AI Engineer to join a new and growing Data & AI function. You'll work hands-on with models and data architecture, with direct impact on both product-facing AI capabilities and internal data infrastructure. This is a deeply technical role: you'll take ML models from research through production, design scalable pipelines, and collaborate closely with R&D and Product to solve real security problems with AI.
Responsibilities:
Design and implement data architecture, pipelines, and governance foundations
Develop and deploy ML models into production (not just POCs), focusing on areas like vulnerability detection, attack emulation, and security insights
Own model lifecycle end to end: research and experimentation, production deployment, monitoring, and iteration
Partner with Product and R&D to identify and execute high-impact AI use cases within our platform
Evaluate build vs. buy vs. fine-tune decisions for AI capabilities
Define and maintain data quality standards and access policies.