לוח משרות דרושים איש ביג דאטה Big Data

At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we have pioneered the world's first fully automated suite of vehicle inspection systems. At the heart of this innovation lies our advanced AI-driven technology, representing the pinnacle of machine learning, GenAI, and computer vision within the automotive sector. With close to $400M in funding and strategic partnerships with industry giants such as Amazon, General Motors, Volvo, and CarMax, UVeye stands at the forefront of automotive technological advancement. Our growing global team of over 200 employees is committed to creating a workplace that celebrates diversity and encourages teamwork. Our drive for innovation and pursuit of excellence are deeply embedded in our vibrant company culture, ensuring that each individual's efforts are recognized and valued as we unite to build a safer automotive world.
We are looking for a Data Operations Group Manager to join our R&D team and manage a team of data taggers and validators responsible for tagging and labeling data for machine learning models.
A day in the life and how you’ll make an impact:
* Managing 4 teams (20+ employees) of data taggers in Israel and the US.
* Ensure that the data is accurately tagged and labeled.
* Ensure that the team is meeting its goals and deadlines.
* Managing an outsourced team that's performing the scale data tagging and labeling.
* Work with the data science team to ensure that the data is being used effectively.
* Develop and implement processes to improve the efficiency and accuracy of the data tagging process.
* Train new team members on the data tagging process.

We are seeking an experienced Data Platform Engineer to join our Storage Analytics team. You will design and build data solutions that provide critical insights into storage performance and usage across Apple's entire device ecosystem.
Description:
Working with large-scale telemetry data from millions of Apple devices worldwide, you'll support multiple CoreOS Storage teams, including Software Update, Backup/Restore/Migration, Storage Attribution, and other storage domains.
Responsibilities
Design, build, and maintain scalable data processing infrastructure to handle large-scale telemetry from Apple's global device fleet
Develop highly scalable data pipelines to ingest and process storage performance metrics, usage patterns, and system telemetry with actionable alerting and anomaly detection
Build and maintain robust data platforms and ETL frameworks that enable CoreOS Storage teams to access, process, and derive insights from storage telemetry data
Engineer automated data delivery systems and APIs that serve processed storage metrics to various engineering teams across different storage domains

Shape the Future of Data - Join our mission to build the foundational pipelines and tools that power measurement, insights, and decision-making across our product, analytics, and leadership teams.
Develop the Platform Infrastructure - Build the core infrastructure that powers our data ecosystem including the Kafka events-system, DDL management with Terraform, internal data APIs on top of Databricks, and custom admin tools (e.g. Django-based interfaces).
Build Real-time Analytical Applications - Develop internal web applications to provide real-time visibility into platform behavior, operational metrics, and business KPIs integrating data engineering with user-facing insights.
Solve Meaningful Problems with the Right Tools - Tackle complex data challenges using modern technologies such as Spark, Kafka, Databricks, AWS, Airflow, and Python. Think creatively to make the hard things simple.
Own It End-to-End - Design, build, and scale our high-quality data platform by developing reliable and efficient data pipelines. Take ownership from concept to production and long-term maintenance.
Collaborate Cross-Functionally - Partner closely with backend engineers, data analysts, and data scientists to drive initiatives from both a platform and business perspective. Help translate ideas into robust data solutions.
Optimize for Analytics and Action - Design and deliver datasets in the right shape, location, and format to maximize usability and impact - whether thats through lakehouse tables, real-time streams, or analytics-optimized storage.
You will report to the Data Engineering Team Lead and help shape a culture of technical excellence, ownership, and impact.

Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructuresomeone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacksits about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakesdesigned for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object storebacked data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.

We are seeking a Big Data Engineer with at least 3 years of experience. A strong understanding of data structure is critical.
You will design, develop and maintain data systems, optimize pipelines, and ensure data integrity.
Data Engineers shape and improve leading mobile analytics solutions while using cutting edge technologies such as Scala, Spark, Hadoop, Astronomer, BigQuery, and many more.
What youll do
Own and develop our products and features end-to-end - from design to production
Collaborate with Product Managers and Software Engineers to deliver complex products.
Develop and maintain scalable data pipelines.
Implement ETL processes and data integration.
Optimize data performance and ensure security.

We are looking for a skilled and motivated Big Data Engineer to join our backend data infrastructure team. You will be at the core of Finals data ecosystem, building and maintaining high-performance data services and pipelines that support both real-time and batch workloads. Your work will directly impact how data is accessed and leveraged across the company from live production environments to ML training pipelines. You will design and maintain systems that span hybrid infrastructure (on-prem and cloud), and ensure our data platform is fast, reliable, and scalable. We value engineers who are curious, open-minded, and excited to learn new technologies and practices as the landscape evolves.



As a Big Data Engineer, you will:

Design, implement, and maintain backend services for managing and processing large-scale data.
Build and operate production-grade data pipelines and infrastructure.
Develop utilities ,libraries and services to support high-throughput data retrieval and access patterns.
Ensure observability, stability, and performance of data services in hybrid (cloud/on-prem) environments.
Monitor and troubleshoot issues in live systems and continuously improve their robustness.
Work cross-functionally to ensure data is accessible, well-modeled, and easy to consume by other teams.