לוח משרות דרושים Senior BigData Developer

We are looking for a Senior Big Data Engineer to join our growing ML Platform team. This is a great opportunity for an experienced Data Engineer who wants to apply their skills to the world of Machine Learning. Together, well provide the high-throughput tools and distributed systems needed to process billions of data points, ensuring that our models have the feature-rich, reliable data they need to perform in real-time.
ML is central to our work. It enables us to process billions of dollars worth of e-commerce transactions and identify fraud rings. Precision at scale is crucial-our models depend on engineered features derived from massive datasets. As the volume of data and the complexity of these models grow, we need senior engineers who can solve the "Big Data" challenges inherent in modern MLOps.
Why should you join us?
Youll be part of a highly proficient engineering team that is the focal point for all ML engineering activity. This role is perfect for a Big Data specialist who wants to own the data-intensive side of ML-building the pipelines and infrastructure that transform raw global traffic into actionable intelligence.
What you will be doing:
Designing and building the data-heavy components of our ML infrastructure, allowing our models to make billions of real-time decisions every year.
Building distributed data processing pipelines that power model training, feature engineering, and offline research at scale.
Optimizing the "Feature Store" concept-ensuring that the data used for training matches the data used in production for real-time serving.
Acting as a technical consultant to data scientists and researchers, helping them navigate massive datasets and providing the Spark-based tooling they need to iterate faster.
Expanding our ML data infrastructure to be scalable and efficient, focusing on the performance of data ingestion and transformation for diverse model types.
Improving MLOps standards by ensuring data reproducibility, lineage, and quality across the entire model lifecycle.

We are looking for a talented Senior Big Data Developer.
Join a core team of experts responsible for developing innovative cyber-attack techniques for Cloud-based environments (AWS, Azure, GCP, Kubernetes) that integrate into our fully automated attack simulation.
This role provides the opportunity to lead end-to-end design & implementation of advanced server-side features using a wide range of technologies: Apache Spark, Apache Airflow, Scala, k8s, Node.js (JS/Typescript) with MongoDB, Redis, Kafka, Dockers and Flink (Big Data Stream Processing)
If you are up for the challenge and you have the X factor, come and join us!

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure-someone 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 stacks-its 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 lakes-designed 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 store-backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.