לוח משרות דרושים מהנדס/ת דאטה/DATA ENGINEER בתל אביב יפו

We are looking for an experienced and visionary Data Platform Engineer to help design, build and scale our BI platform from the ground up.
In this role, you will be responsible for building the foundations of our data analytics platform - enabling scalable data pipelines and robust data modeling to support real-time and batch analytics, ML models and business insights that serve both business intelligence and product needs.
You will be part of the R&D team, collaborating closely with engineers, analysts, and product managers to deliver a modern data architecture that supports internal dashboards and future-facing operational analytics.
If you enjoy architecting from scratch, turning raw data into powerful insights, and owning the full data lifecycle - this role is for you!
Responsibilities
Take full ownership of the design and implementation of a scalable and efficient BI data infrastructure, ensuring high performance, reliability and security.
Lead the design and architecture of the data platform - from integration to transformation, modeling, storage, and access.
Build and maintain ETL/ELT pipelines, batch and real-time, to support analytics, reporting, and product integrations.
Establish and enforce best practices for data quality, lineage, observability, and governance to ensure accuracy and consistency.
Integrate modern tools and frameworks such as Airflow, dbt, Databricks, Power BI, and streaming platforms.
Collaborate cross-functionally with product, engineering, and analytics teams to translate business needs into data infrastructure.
Promote a data-driven culture - be an advocate for data-driven decision-making across the company by empowering stakeholders with reliable and self-service data access.

In this role, youll own the engineering backbone that enables our data engineers & data scientists to experiment, innovate, and deploy production-ready models, ensuring our systems are robust, scalable, and future-proof.

What You'll Actually Do:

Lead and mentor a team of Data Engineers, fostering a culture of technical excellence, accountability, and collaboration.
Define and drive the teams technical roadmap, standards, and best practices for data modeling, performance, and reliability.
Design, build, and evolve scalable embedding pipelines and recommendation infrastructure, from algorithm tuning and training workflows to production deployment.
Manage and optimize vector db, ensuring efficient indexing strategies, search performance, and low-latency retrieval.
Lead the development of retrieval services leveraging Approximate Nearest Neighbor (ANN/KNN) techniques such as HNSW, IVF, and Product Quantization (PQ).
Ensure reliability, observability, and scalability across all real-time data systems powering our recommendation engine.
Collaborate with Data Science, Product, and Analytics to operationalize models, validate KPIs, and translate technical insights into actionable product improvements.

As a Senior BI Data Engineer, youll join a company where culture isnt a slogan - its our DNA.

Youll be part of a data-driven organization where every voice matters, working at the heart of our BI team to raise the bar for analytical excellence.

In this role, youll take end-to-end ownership of complex, high-impact BI initiatives, shaping how measures success, makes decisions, and scales. Your work will directly impact over 1M users worldwide, empowering B2B sales teams to unlock new revenue opportunities and drive sustainable growth.

What Youll Actually Do:

Lead the design and evolution of BI data foundations, owning key product and GTM metric definitions and data models
Build, scale, and maintain high-quality ELT pipelines and curated datasets (raw → modeled → semantic) that power dashboards and self-serve analytics
Collaborate closely with Data Scientists to operationalize machine learning use cases, including feature pipelines and analytical datasets
Take senior ownership within the BI team by mentoring peers, reviewing critical work, and promoting best practices in analytics engineering and semantic modeling
Own the performance, cost, and reliability of the data warehouse and BI layer by optimizing models, queries, and incremental processing patterns
Translate ambiguous business questions into clear, governed metrics and scalable data models, partnering with Product, RevOps/GTM, Finance, and Analytics
Establish and maintain strong data quality, observability, and monitoring practices (tests, SLAs, anomaly detection)
Drive standardization and automation across the BI development lifecycle, including version control, CI/CD, documentation, and release processes
Stay ahead of modern BI and analytics engineering trends, including AI-assisted development, and apply them pragmatically to increase trust and speed

Were seeking our first Data Engineer to join the Revenue Operations team. This is a high-impact role where youll build the foundations of our data infrastructure - connecting the dots between systems, designing and maintaining our data warehouse, and creating reliable pipelines that bring together all revenue-related data. Youll work directly with the Director of Revenue Operations and partner closely with Sales, Finance, and Customer Success.
This is a chance to shape the role from the ground up and create a scalable data backbone that powers smarter decisions across the company.
Role Overview:
As the Data Engineer, you will own the design, implementation, and evolution of our data infrastructure. Youll connect core business systems (CRM, finance platforms, billing systems,) into a central warehouse, ensure data quality, and make insights accessible to leadership and revenue teams. Your success will be measured by the accuracy, reliability, and usability of the data foundation you build.
Key Responsibilities:
Data Infrastructure & Warehousing:
Design, build, and maintain a scalable data warehouse for revenue-related data.
Build ETL/ELT pipelines that integrate data from HubSpot, Netsuite, billing platforms, ACP, and other business tools.
Develop a clear data schema and documentation that can scale as we grow.
Cross-Functional Collaboration:
Work closely with Sales, Finance, and Customer Success to understand their reporting and forecasting needs.
Translate business requirements into data models that support dashboards, forecasting, and customer health metrics.
Act as the go-to partner for data-related questions across revenue teams.
Scalability & Optimization:
Continuously monitor and optimize pipeline performance and warehouse scalability.
Ensure the infrastructure can handle increased data volume and complexity as the company grows.
Establish and enforce best practices for data quality, accuracy, and security.
Evaluate and implement new tools, frameworks, or architectures that improve automation, speed, and reliability.
Build reusable data models and modular pipelines to shorten development time and reduce maintenance.

We use cutting-edge innovations in financial technology to bring leading data and features that allow individuals to be qualified instantly, making purchases at the point-of-sale fast, fair and easy for consumers from all walks of life.
As part of our Data Engineering team, you will not only build scalable data platforms but also directly enable portfolio growth by supporting new funding capabilities, loan sales and securitization, and improving cost efficiency through automated and trusted data flows that evolve our accounting processes.
Responsibilities
Design and build data solutions that support our companys core business goals, from enabling capital market transactions (loan sales and securitization) to providing
reliable insights for reducing the cost of capital.
Develop advanced data pipelines and analytics to support finance, accounting, and product growth initiatives.
Create ELT processes and SQL queries to bring data to the data warehouse and other data sources.
Develop data-driven finance products that accelerate funding capabilities and automate accounting reconciliations.
Own and evolve data lake pipelines, maintenance, schema management, and improvements.
Create new features from scratch, enhance existing features, and optimize existing functionality.
Collaborate with stakeholders across Finance, Product, Backend Engineering, and Data Science to align technical work with business outcomes.
Implement new tools and modern development approaches that improve both scalability and business agility.
Ensure adherence to coding best practices and development of reusable code.
Constantly monitor the data platform and make recommendations to enhance architecture, performance, and cost efficiency.

we are looking for a Data Integration Engineer.
As a Data Integration Engineer , you will use technical skills, analytical insights, and a business perspective to ensure we have the most accurate and complete data from each client to feed our data science models. You will be responsible for helping new and existing customers ensure they are receiving the best performance possible and leading the charge in processing the data of the largest eCommerce merchants in the world.
What You'll Be Doing:
Maintain/manage merchant data configuration layer of platform (automated data tagging, transformation, filtering, etc.)
Combine technical, business, and analytical objectives to come up with tailored solutions for onboarding merchants
Perform due diligence on merchant websites, business models, and data flows
Generate tailored data templates (JSON, CSV) to help guide merchant developers/data teams in building high-quality online (API) and offline (file transfer) integrations.
Write queries and scripts using Python, SQL, Spark, Databricks, or R to test, validate, and detect data issues in API calls within both sandbox and production environments. Additionally, transform and upload large historical data files to APIs
Ad hoc data wrangling and sanitization
Monitor automated data alerts - prioritize, track, and communicate merchant data issues/irregularities with internal and external stakeholders
Work closely with analysts, research, and data science teams to accommodate changes in industry data
Identify, research, and offer system-wide solutions for common data issues
Work closely with client-facing teams to engage customers regarding their data integrations
Support R&D, Product Management, Data Science, Integrations Engineering, Account Management, and Sales as a subject matter expert of product, technology, and merchants

we are looking for a Product Owner -Software Engineer.
As a Product Owner / Developer, you will play a dual role - defining business and product requirements while contributing directly to the technical delivery. Youll partner with collaborators across engineering, data, and business teams to translate goals into actionable roadmaps and measurable results.
Key Responsibilities:
Gather and define requirements aligned with strategic business goals and customer value.
Help the team leaders maintain and prioritize the product backlog, ensuring clarity, transparency, and focus on impact.
Collaborate with engineering teams to design and build scalable data and automation solutions (50%+ of the time)
Contribute to development, testing, and deployment of modern data pipelines using Apache Airflow, Apache Spark, and AWS cloud services (ECS, Lambda, Glue, S3).
Balance short-term delivery objectives with long-term technical vision.
Track and communicate key performance metrics that demonstrate business outcomes.

As a Data Engineer at Meta, you will shape the future of people-facing and business-facing products we build across our entire family of applications. Your technical skills and analytical mindset will be utilized designing and building some of the world's most extensive data sets, helping to craft experiences for billions of people and hundreds of millions of businesses worldwide.
In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user experience for our 3 billion plus users, as well as our internal employee community.
You will be at the forefront of identifying and solving some of the most interesting data challenges at a scale few companies can match. By joining Meta, you will become part of a world-class data engineering community dedicated to skill development and career growth in data engineering and beyond.
Data Engineering: You will guide teams by building optimal data artifacts (including datasets and visualizations) to address key questions. You will refine our systems, design logging solutions, and create scalable data models. Ensuring data security and quality, and with a focus on efficiency, you will suggest architecture and development approaches and data management standards to address complex analytical problems.
Product leadership: You will use data to shape product development, identify new opportunities, and tackle upcoming challenges. You'll ensure our products add value for users and businesses, by prioritizing projects, and driving innovative solutions to respond to challenges or opportunities.
Communication and influence: You won't simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.
Data Engineer, Product Analytics Responsibilities
Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way
Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
Define and manage Service Level Agreements for all data sets in allocated areas of ownership
Solve challenging data integration problems, utilizing optimal Extract, Transform, Load (ETL) patterns, frameworks, query techniques, sourcing from structured and unstructured data sources
Improve logging
Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts
Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts
Influence product and cross-functional teams to identify data opportunities to drive impact.

As a Data Engineer at Meta, you will shape the future of people-facing and business-facing products we build across our entire family of applications. Your technical skills and analytical mindset will be utilized designing and building some of the world's most extensive data sets, helping to craft experiences for billions of people and hundreds of millions of businesses worldwide.
In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user experience for our 3 billion plus users, as well as our internal employee community.
You will be at the forefront of identifying and solving some of the most interesting data challenges at a scale few companies can match. By joining Meta, you will become part of a world-class data engineering community dedicated to skill development and career growth in data engineering and beyond.
Data Engineering: You will guide teams by building optimal data artifacts (including datasets and visualizations) to address key questions. You will refine our systems, design logging solutions, and create scalable data models. Ensuring data security and quality, and with a focus on efficiency, you will suggest architecture and development approaches and data management standards to address complex analytical problems.
Product leadership: You will use data to shape product development, identify new opportunities, and tackle upcoming challenges. You'll ensure our products add value for users and businesses, by prioritizing projects, and driving innovative solutions to respond to challenges or opportunities.
Communication and influence: You won't simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.
Data Engineer, Product Analytics Responsibilities
Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve
Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way
Define and manage Service Level Agreements for all data sets in allocated areas of ownership
Determine and implement the security model based on privacy requirements, confirm safeguards are followed, address data quality issues, and evolve governance processes within allocated areas of ownership
Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
Solve our most challenging data integration problems, utilizing optimal Extract, Transform, Load (ETL) patterns, frameworks, query techniques, sourcing from structured and unstructured data sources
Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts
Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts.

As a Data Engineer at Meta, you will shape the future of people-facing and business-facing products we build across our entire family of applications (Facebook, Instagram, Messenger, WhatsApp, Reality Labs, Threads). Your technical skills and analytical mindset will be utilized designing and building some of the world's most extensive data sets, helping to craft experiences for billions of people and hundreds of millions of businesses worldwide.
In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user experience for our 3 billion plus users, as well as our internal employee community.
You will be at the forefront of identifying and solving some of the most interesting data challenges at a scale few companies can match. By joining Meta, you will become part of a world-class data engineering community dedicated to skill development and career growth in data engineering and beyond.
Data Engineering: You will guide teams by building optimal data artifacts (including datasets and visualizations) to address key questions. You will refine our systems, design logging solutions, and create scalable data models. Ensuring data security and quality, and with a focus on efficiency, you will suggest architecture and development approaches and data management standards to address complex analytical problems.
Product leadership: You will use data to shape product development, identify new opportunities, and tackle upcoming challenges. You'll ensure our products add value for users and businesses, by prioritizing projects, and driving innovative solutions to respond to challenges or opportunities.
Communication and influence: You won't simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.
Data Engineer, Product Analytics Responsibilities
Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve
Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights visually in a meaningful way
Define and manage Service Level Agreements for all data sets in allocated areas of ownership
Determine and implement the security model based on privacy requirements, confirm safeguards are followed, address data quality issues, and evolve governance processes within allocated areas of ownership
Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
Solve our most challenging data integration problems, utilizing optimal Extract, Transform, Load (ETL) patterns, frameworks, query techniques, sourcing from structured and unstructured data sources
Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts
Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts.

we are seeking a Senior Data Infra Engineer. You will be responsible for designing and building all data, ML pipelines, data tools, and cloud infrastructure required to transform massive, fragmented data into a format that supports processes and standards. Your work directly empowers business stakeholders to gain comprehensive visibility, automate key processes, and drive strategic impact across the company.
Responsibilities
Design and Build Data Infrastructure: Design, plan, and build all aspects of the platform's data, ML pipelines, and supporting infrastructure.
Optimize Cloud Data Lake: Build and optimize an AWS-based Data Lake using cloud architecture best practices for partitioning, metadata management, and security to support enterprise-scale operations.
Lead Project Delivery: Lead end-to-end data projects from initial infrastructure design through to production monitoring and optimization.
Solve Integration Challenges: Implement optimal ETL/ELT patterns and query techniques to solve challenging data integration problems sourced from structured and unstructured data.

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're seeking a Data Engineer to architect and develop sophisticated data solutions using advanced Spark, PySpark, Databricks and EMR implementations in our mission to transform the cyber-security breach readiness and response market.

Join us in crafting cutting-edge solutions for the cyber world using Spark/PySpark ETLs and data flow processes. Dive into the realm of multi-Cloud environments while collaborating closely with investigators to fine-tune PySpark performance. Harness the power of top-notch technologies like Databricks to elevate our technical projects, scaling them for efficiency. Embrace innovation as you research and implement new techniques. Evolve with us as a key member of the R&D team.

Technical Impact:
Design and implement complex data processing architectures for cloud security analysis.
Optimize and scale critical PySpark workflows across multi-cloud environments.
Develop innovative solutions for processing and analyzing massive security datasets.
Drive technical excellence through sophisticated ETL implementations.
Contribute to architectural decisions and technical direction.

Core Responsibilities:
Build robust, scalable data pipelines for security event processing.
Optimize performance of large-scale PySpark operations.
Implement advanced data solutions using Databricks and cloud-native technologies.
Research and prototype new data processing methodologies.
Provide technical guidance and best practices for data engineering initiatives.

Location: Tel Aviv, IL.
Hybrid work environment.

Were looking for a Data Engineer to join our BI Team within the R&D department. This role is all about building and maintaining robust data pipelines and enabling data-driven decision-making across all departments.

Responsibilities:
Develop and maintain ETL processes and data pipelines to ensure smooth, accurate data flow.
Designed, built, and managed Airflow DAGs to orchestrate complex data workflows and ensure reliable execution of ETL and analytics tasks
Design and optimize relational databases, including conceptual and physical data modeling (ERD), schema design, and performance tuning to support scalable analytics and business applications.
Collaborate with cross-functional teams to translate business needs into technical solutions.
Build and maintain automation processes powered by algorithms to optimize workflows for reducing manual effort.
Build and maintain custom data scripts for integrations with brand and publisher partners
Support data visualization efforts through BI tools.
Gain in-depth understanding of discrepancy processes across departments and improve data accuracy.

We are building next-generation GenAI security intelligence and SaaS Security Posture Management (SSPM) solutions that protect enterprises worldwide. If you enjoy turning complex security data into actionable insights and delivering end-to-end systems, this role is for you.

About the role
You will own, build, and maintain our Pythonic data pipeline and enrichment system on top of PostgreSQL and BigQuery. This system powers security analytics, detections, and intelligence. A core part of your job will be to design and implement new components, improve reliability and performance, and ensure data quality and observability.



Key Responsibilities
Own, build, and maintain production data pipelines and enrichment services using Python, PostgreSQL, and BigQuery.
Architect data systems end to end, including design, deployment, monitoring, and iterative improvement.
Analyze complex security datasets and SaaS telemetry to uncover risks, patterns, and opportunities.
Research emerging threat vectors and contribute to automated intelligence feeds and published reports.
Work across security domains such as SSPM, Shadow Integrations, DLP, and GenAI Protection.