דרושים » דאטה » Data Engineer

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

We're looking for a Senior Data Infrastructure Engineer to help us build the data foundation that powers everything does. You'll be joining the Data Platform team in TLV, working on the infrastructure that ingests, processes, and governs data across a growing stack of products, customers, and microservices - structured and unstructured, streaming and batch. The work you do here sits at the core of how every team makes decisions.
We believe three things matter for every role : drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.
Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role youll:
Design and implement data solutions for all application requirements in a distributed microservices environment
Build ingestion layers and a data lake using streaming ETLs and Change Data Capture
Implement large-scale batch and streaming pipelines with modern data processing frameworks
Build pipelines and scheduling infrastructures that other teams rely on every day
Ensure data quality, compliance, and governance across entire data platform
Help shape data-mesh concepts that empower other teams to leverage data independently
Partner with Data Engineers, ML Engineers, Data Scientists, BI Engineers, and Product Managers to move fast and build right

As a Senior Data Engineer, you will play a key role in owning and scaling the backend data infrastructure that powers our platform-supporting real-time optimization, advanced analytics, and machine learning applications.

What You'll Do

Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark, and other modern tools.
Own the backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets.
Leverage Google Cloud Platform (GCP) services to architect and operate scalable, secure, and cost-effective data solutions across the pipeline lifecycle.
Develop and optimize ETL/ELT workflows across multiple environments to support internal applications, analytics, and machine learning workflows.
Build and maintain data marts and data models with a focus on performance, data quality, and long-term maintainability.
Collaborate with cross-functional teams including development teams, product managers, and external stakeholders to understand and translate data requirements into scalable solutions.
Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability.

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 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 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.

Were looking for a Big Data Engineer to develop and integrate systems that retrieve, process and analyze data from around the digital world, generating customer-facing data. This role will report to our Team Manager, R&D.
Why is this role so important?
A data-focused company, and data is the heart of our business.
As a big data engineer developer, you will work at the very core of the company, designing and implementing complex high scale systems to retrieve and analyze data from millions of digital users.
Your role as a big data engineer will give you the opportunity to use the most cutting-edge technologies and best practices to solve complex technical problems while demonstrating technical leadership.
So, what will you be doing all day?
Your role as part of the R&D team means your daily responsibilities may include:
Design and implement complex high scale systems using a large variety of technologies.
You will work in a data research team alongside other data engineers, data scientists and data analysts. Together you will tackle complex data challenges and bring new solutions and algorithms to production.
Contribute and improve the existing infrastructure of code and data pipelines, constantly exploring new technologies and eliminating bottlenecks.
You will experiment with various technologies in the domain of Machine Learning and big data processing.
You will work on a monitoring infrastructure for our data pipelines to ensure smooth and reliable data ingestion and calculation.

We're looking for a Data Warehouse Tech Lead to drive the technical vision and execution of our data infrastructure that powers decision-making across .
You'll lead both the technology and the business coordination for our data warehouse - architecting scalable solutions while working closely with stakeholders and data providers to ensure our platform serves the entire organization's needs. This role combines deep technical leadership with strategic business partnership as we build next-generation data stack.
We believe three things matter for every role : drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.
Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll
Lead technical architecture - design and develop scalable data warehouse solutions that support multiple products and serve the entire organization's analytics needs
Manage the technical roadmap - set strategy and guide execution for the Data Warehouse team, ensuring our platform evolves with business requirements
Drive business process coordination - translate business needs into technical requirements while establishing clear data contracts with R&D, Analytics, and external data providers
Establish and implement best practices - set technical standards for data warehouse architecture, performance tuning, and development methodologies that guide the entire team's approach to building scalable data solutions
Create and maintain sustainable data pipelines - build resilient systems capable of handling unstructured data and managing an evolving schema registry across diverse data sources
Implement advanced data modeling - create robust data structures using methodologies like dimensional modeling, and optimize ETL/ELT processes for our semantic layer
Establish data quality standards - build processes for schema evaluation, anomaly detection, and monitoring data completeness and freshness across all sources
Lead cross-team collaboration - work directly with Data Engineers, ML Platform Engineers, Data Scientists, Analysts, and Product Managers to align technical solutions with business goals

we are looking for a Data Infrastructure Engineer.
Responsibilities:
Design and build data solutions that support 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.