לוח משרות דרושים מהנדס/ת דאטה/DATA ENGINEER משרה מלאה, לחיפוש זה נמצאו 135 משרות

we are looking for a Senior Full-Stack Developer.
Responsibilities:
- Design and build scalable backend services in Python.
- Architect large-scale, distributed systems for reliability and performance.
- Develop and maintain frontend features using TypeScript and React.
- Own features end-to-end, from backend services to modern web interfaces.
- Collaborate with product, design, and engineering teams to ship features end-to-end.
- Contribute to technical decisions, code reviews, and engineering best practices.

We are looking for an experienced Network professional to join our Network Data-Center Team.
עוד על התפקיד
Sizing, designing, implementing and maintaining network data-center in a large-scale enterprise
Planning and writing LLD/HLD documents integrating network data-center solutions
Operating and troubleshooting various network systems.

We are seeking an experienced and professional Data Engineer to join our technology team.
The role involves designing, developing, and implementing data engineering solutions, working with big data systems, integrating systems, managing cloud-based data infrastructure, and leveraging AI tools to enhance data processing and analytics.
the ideal candidate will have proven experience with advanced technologies, including AI-driven tools, and the ability to work in a dynamic and challenging environment.

We are looking for a strong, hands-on Data Engineer to join our team and play a key role in building our data infrastructure from the ground up. In this role, you will design and implement scalable data pipelines and platforms, supporting both batch and real-time use cases. You will work closely with analysts and stakeholders to deliver reliable, high-quality data solutions, and take full ownership of data flows - from ingestion to consumption. This is a great opportunity for an executor who enjoys building, moving fast, and making an impact.
What will your job look like?
Design, build, and maintain robust and scalable data pipelines (batch and real-time) end-to-end.
Design and implement scalable, flexible data architectures to support evolving business needs.
Build and manage data platforms, including data lakes and data warehouses.
Integrate multiple data sources (structured and unstructured) into a unified data platform using batch (ETL) and real-time streaming solutions.
Design and implement efficient data models, schemas, and database structures (SQL / NoSQL).
Develop and implement data quality processes to ensure accuracy, consistency, and reliability.
Monitor, optimize, and troubleshoot data infrastructure to meet performance and SLA requirements.

We are looking for a strong, hands-on Data Platform Engineer to join our team and play a key role in building our data infrastructure from the ground up.
In this role, you will design and implement scalable data pipelines and platforms, supporting both batch and real-time use cases.
You will work closely with analysts and stakeholders to deliver reliable, high-quality data solutions, and take full ownership of data flows - from ingestion to consumption.
This is a great opportunity for an executor who enjoys building, moving fast, and making an impact.
What will your job look like?
Design, build, and maintain the underlying infrastructure for a modern cloud-based data platform.
Implement and manage CI/CD processes for data pipelines and platform deployments across development and production environments.
Design and manage secure, scalable AWS-based data infrastructure, including IAM roles, permissions, policies, networking, and environment isolation.
Build and maintain orchestration, monitoring, alerting, and observability capabilities for data pipelines and platform services.
Support deployment, reliability, and operational excellence of data workloads running on technologies such as Spark, DBT, Airflow, Athena, and AWS services.
Collaborate closely with Data Engineers, Analysts, BI teams, and IT/Cyber teams to ensure secure and scalable data operations.
Monitor, troubleshoot, and optimize platform performance, availability, and cost efficiency.
Establish best practices for infrastructure-as-code, deployment standards, security, and production readiness.

We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

Required Senior Data & ML Infrastructure Engineer
What will your job look like?
Optimize distributed data and evaluation pipelines for memory efficiency, throughput, and training performance.
Build tools for data exploration, manipulation, validation, and quality assessment.
Own model manipulation, packaging, distribution workflows, and interfaces with downstream deployment and EyeQ integration.
Coordinate data contracts and workflows with researchers, infrastructure and data teams, and internal customers across.

We are looking for a Data Science Engineer to design and implement the decision-making logic of our Price Optimizer. You will focus on translating complex business rules into mathematical models and production-ready Python code.

Your primary mission is to build the "brain" of our system. You will work at the intersection of Data Science and Product, ensuring our simulation and optimization engines accurately reflect real-world pricing strategies and market dynamics. While you will write production code, you will rely on our Data Engineers for ETL pipelines orchestration and distributed compute scaling.

Responsibilities:

Implement Business Logic: Translate intricate pricing rules and commercial strategies into robust Python code and mathematical constraints.
Refine Optimization Models: Develop and tune the simulation and revenue management algorithms that drive our pricing recommendations.
Write Clean Code: Contribute high-quality, tested, and maintainable code to the core logic repositories.
Analyze & Improve: Use data to validate model behavior and identify edge cases where business rules clash with algorithmic outputs.
Collaborate: Partner with Solution Architects to define logic requirements and with Data Engineers to integrate your models into the Dagster pipelines.

We are looking for a Data Science Engineer to design and implement the decision-making logic of our Price Optimizer. You will focus on translating complex business rules into mathematical models and production-ready Python code.

Your primary mission is to build the "brain" of our system. You will work at the intersection of Data Science and Product, ensuring our simulation and optimization engines accurately reflect real-world pricing strategies and market dynamics. While you will write production code, you will rely on our Data Engineers for ETL pipelines orchestration and distributed compute scaling.

Responsibilities:

Implement Business Logic: Translate intricate pricing rules and commercial strategies into robust Python code and mathematical constraints.
Refine Optimization Models: Develop and tune the simulation and revenue management algorithms that drive our pricing recommendations.
Write Clean Code: Contribute high-quality, tested, and maintainable code to the core logic repositories.
Analyze & Improve: Use data to validate model behavior and identify edge cases where business rules clash with algorithmic outputs.
Collaborate: Partner with Solution Architects to define logic requirements and with Data Engineers to integrate your models into the Dagster pipelines.

We're not looking for someone who fits neatly into a box. We're looking for someone who finds neat boxes a little underwhelming.
You're a builder, not just an advisor - analysis without artifacts isn't enough.
You close loops. You ship things - You leave systems better than you found them.
You're customer-obsessed - you understand that what happens in enterprise POVs isn't a distraction from the real work. It is the real work. You run toward hard customer problems rather than away from them.
You're opinionated and direct - you push back when something is wrong, change your mind when shown better evidence, and write and speak clearly without filler.
You move without waiting to be told - when something is broken or missing, you identify it, own it, and fix it. You don't need a clean scope to start. You operate well under ambiguity and produce clarity for everyone around you.
You have a hacker mentality - you find the path through, you're comfortable in ambiguous and underdocumented systems, and you debug by instinct as much as by method.
You're technically deep and contextually wide - you can go deep on a data pipeline, a detection rule, a query engine, or a cloud architecture, then explain any of it to an executive the same afternoon.

As a Senior Data Engineer , you will play a key role in building and scaling the companys data infrastructure. Youll be responsible for creating the foundation that enables teams across the organization to access, analyze, and leverage data effectively. Working closely with Data Science, Delivery, Product, GTM, and Analytics teams, youll design and maintain data pipelines, manage our data warehouse, and transform raw data into structured, reliable assets that power business and product decisions.



What Youll Do

Build and Manage Data Infrastructure: Own and maintain Nomas data warehouse infrastructure, ensuring scalability, reliability, and performance
Develop Data Pipelines: Design, build, and maintain data pipelines that transform company data into structured, queryable assets
Enable Data-Driven Decisions: Partner with Engineering, Data Science, Product and Analytics teams to deliver trusted data foundations and actionable insights.
Create BI Dashboards: Build and maintain dashboards and reporting layers that support product, and operational decision-making
Ensure Data Quality and Accessibility: Implement best practices for data modeling, governance, monitoring, and reliability

Were looking for engineers who combine strong systems skills with product sense: understanding who uses the platform, why certain capabilities matter, and making pragmatic trade-offs to maximize impact. On our team, engineering work is expected to be connected to real users and outcomes - youll regularly align with internal stakeholders, clarify requirements, and help drive prioritization.

In this role, you will:

Design and implement new functionality in YTsaurus core (C++) with production reliability in mind.
Build and evolve platform-level capabilities: platform architecture and operating model-multi-cluster growth, shared primitives, and a consistent experience that scales with new teams and use cases.
Improve end-to-end platform experience for internal (and external-facing) users: APIs, guardrails, debugging workflows, and automation.
Own production quality: incident response / on-call rotation, root cause analysis, and turning learnings into durable fixes.
Example projects
Roll out sharded YTsaurus masters (incl. Kubernetes operator support) and build automatic balancing of metadata across master cells (consensus groups) to remove control-plane bottlenecks and unlock 10-100x cluster growth.
Make CHYT interactive SQL faster and more predictable at high load via performance work like data-skipping / min-max-style indexes and improved execution introspection.
Turn Orchestracto into a platform product by defining the building blocks, developer experience, and governance for how teams create and share workflows.
Scale and harden Parquet-on-S3 for native YTsaurus workloads by tackling replication/movement, consistent lifecycle semantics, and master-server metadata optimizations for performance and reliability.
Design and ship complete, trustworthy audit trails for data changes (who/what/when) across heterogeneous storage and compute paths.
Tech stack
Core: modern C++ (C++20, async + multithreaded primitives)
Services & tooling: Go and Python (microservices, utilities, integration tests)

We are looking for a strong Data Engineer to be part of one of our most strategic initiatives - building the context graph that powers our AI-SOC platform. Our team is building the context layer behind our AI Agents, transforming raw customer data into meaningful, normalized context that enables smarter autonomous security decisions.

This is a unique opportunity to join a brand-new, small, senior team with high ownership, strong product influence, and the chance to build foundational AI infrastructure from the ground up.

What Youll Be Doing:
Think like a product person - collaborate with stakeholders and consumers to design meaningful data models and continuously validate them against real customer data.
Run POCs work end to end - connect a new data source, ingest and normalize it, correlate entities across systems, and demonstrate the value.
Do the analysis that shapes the model - profile new data, measure coverage and accuracy, and find the gaps that matter to the product.
Integrate identity, endpoint, and security data sources - learn how each system exposes its data and turn it into reliable, consistent models.
Build and own scalable data pipelines, transforming raw data into clean, normalized, and well-tested models using Python, SQL, and dbt.

We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).