דרושים » דאטה » Data Infrastructure Engineer

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.

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.

Were looking for a Senior Data Engineer to own high-impact data products from architecture through production deployment, monitoring, and continuous improvement. This isnt a pure infrastructure role - youll combine strong engineering with product thinking, operational excellence, and awareness of data quality, cost, and business impact.
You will design, implement, test, deploy, and maintain production-grade data products - pipelines, transformation layers, data quality and reliability systems - using tools like DBT (on Spark) and Databricks. Youll apply best practices in Python and SQL to build scalable and maintainable data transformations, and leverage technologies like LLMs and GenAI to create innovative solutions for real business problems.
This role is ideal for someone who wants technical leadership responsibilities in an AI-first engineering culture - we use LLMs, GenAI, and AI-native development tools as core parts of our daily workflow.
Key Responsibilities
Act as a technical leader within the team - raise engineering standards, drive strong architectural choices, and improve how we build
Own data products end-to-end: design, development, deployment, monitoring, and iteration
Work closely with senior leadership to translate strategic goals into scalable data solutions
Develop and maintain production ETL/ELT pipelines using DBT (on Spark) and orchestrated workflows in Databricks
Build monitoring, alerting, and testing pipelines to ensure reliability and performance in production
Evaluate and introduce new technologies - including AI-native development tools - and integrate the ones that create real impact
Collaborate with customers and external data providers - gathering requirements and making product decisions.
Mentor team members through code reviews, pairing, and knowledge sharing

Grip Security is looking for a Senior Data Platform Engineer to join our community!
We are a fast-growing startup in the software-as-a-service and AI Security Industry. We provide innovative solutions to securing the whole organization-to-SaaS surface. (More details: https://grip.security)
Using the newest technologies, we're working on solving a huge problem all enterprises face today - to govern the accessibility of all their employees to all 3rd party vendors (GitHub, SendGrid, Atlassian, and thousands more!), and ensure there is no leftover/unwanted access to any of the organization's SaaS and AI assets. The SaaS and AI security field is complex and challenging; therefore, we're looking for super-talented people, who are not afraid of technical challenges and breaking down barriers to achieve good solutions.
The job
As a Senior Data Platform Engineer, you will play a key role in building and evolving Grips modern data platform - the infrastructure that powers product features and analytics across the company.
You will focus on designing and operating scalable, reliable data systems and platform tooling that support our Data Lakehouse, enabling engineers, analysts and research teams to work with data efficiently and with minimal friction.
Responsibilities:
Design, build and operate a cloud-native modern data platform.
Develop and optimize data processing frameworks and pipelines across batch and streaming workloads.
Improve developer experience and platform usability through tooling and automation.
Lead and support large-scale data migrations and architectural improvements.
Drive best practices around infrastructure, CI/CD, testing, and system design.
Collaborate with developers, analysts, data scientists and other stakeholders to develop new products and features.
Contribute to a strong engineering culture of ownership, learning, and knowledge sharing.

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 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.

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 Data Engineer to join our growing team!
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless workers who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
In this role, you will be responsible for:
Designing, building, and maintaining scalable data pipeline architectures
Developing ETL processes to integrate data from multiple sources
Creating and optimizing data models for efficient storage and retrieval
Implementing data quality controls and monitoring systems
Collaborating with data scientists and analysts to deliver data solutions
Building and maintaining data warehouses and data lakes
Performing in-depth data analysis and providing insights to stakeholders
Taking full ownership of data quality, documentation, and governance processes
Building and maintaining comprehensive reports and dashboards
Ensuring data security and regulatory compliance.

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