דרושים » תוכנה » Software Engineer, Data & Platform

We are looking for a Backend Engineer with deep data engineering expertise to build and evolve the systems that power product, analytics, experimentation, and AI development.
You will work across production backend services and data infrastructure, taking technical ownership of the full data lifecycle - from event ingestion and real-time processing to orchestration, modeling, quality, and reliable data access. This role combines hands-on backend development with ownership of shared data platform, working closely with backend, frontend, AI, DevOps, analytics, and product teams.
What you'll do:
Build and maintain production-grade Python and Java backend services, APIs, and reactive data-processing pipelines with clear interfaces, resilient failure handling, and comprehensive observability.
Own and evolve batch and streaming data pipelines supporting product analytics, learner insights, experimentation, and AI model training.
Design and maintain trusted data models, shared metrics, and semantic-layer business logic across the analytical data platform.
Define and enforce standards for data contracts, testing, lineage, freshness, and observability.
Develop reliable approaches to schema evolution, backfills, replay, workload distribution, and failure recovery.
Collaborate with DevOps, backend, frontend, AI, analytics, and product teams to deliver reusable data capabilities and maintain clear system boundaries.

We are looking for an experienced and hands-on Backend Engineer to be a key player in building high-scale Data Platforms and Products for our business teams.
This role involves working with large datasets and scalable systems, and developing internal tools to enable data-driven decision-making across the company.
The team builds the backend systems and data infrastructure the rest of the company relies on: internal products, APIs, event pipelines, and integrations with external tools. The data comes from the full product, at the volume of over 1.7 million daily active users. The features are multi-system: an API, a pipeline, and often LLM-backed flows that have to stay correct in production.
LLMs and agents are part of the job. We use them to write, review, and debug code, and we ship them inside the product: agents and LLM features that other teams depend on.
Key Responsibilities:
Develop internal tools and products for teams across Connecteam.
Build and maintain services and APIs that support complex, multi-step workflows.
Operate in a real-time, event-driven environment at high volume.
Create and manage data pipelines, including the checks that keep data trustworthy.
Design and ship LLM and agent features: tool use, prompts, evaluation, and the limits around cost, latency, and quality.
Use coding agents as part of normal delivery, and raise the quality of what they produce.
Take ownership of multiple systems and products.
Work in a multi-cloud environment (Azure/GCP/AWS).
Integrate third-party tools with our platform.
Translate ambiguous business requirements into a technical plan and lead it through to production.

We're building a new orchestration platform for AI datacenters-software that turns a complex, multi-vendor stack of compute, networking, and accelerators into something customers can deploy, operate, and optimize with confidence.
You'll join a small, growing engineering team where every engineer ships. In this hands-on development role, you'll build backend services, contribute to system design, and own features from implementation through production, working closely with experienced engineers.
If you enjoy solving challenging engineering problems, building new products, and seeing your work run at scale in real AI datacenters, this role is for you.
What You'll Do
- Build and maintain core services and features for an orchestration platform spanning compute, networking, and accelerators.
- Translate customer requirements into APIs, workflows, and data models in collaboration with the team.
- Deliver features end-to-end, from prototyping and implementation to testing, deployment, and production support.
- Write reliable, maintainable code and contribute to shared libraries, automated tests, and CI/CD pipelines.
- Contribute to design discussions and make practical implementation tradeoffs around performance, reliability, and simplicity.
- Participate in code reviews, share knowledge, and help improve engineering practices.
- Troubleshoot issues and improve service performance, observability, and operational readiness.

we are on a mission to create public transportation systems that provide far greater access to jobs, healthcare, and education. Our platform serves as the technology backbone for modern transit networks, transforming antiquated and siloed public transportation systems into smart, data-driven, and efficient digital networks. With hundreds of agency partners around the world, we are recognized as the leading transportation technology and service provider globally.
As a Software & Data Engineer at our company, you will play a central role in building and evolving the data infrastructure that powers one of the world's leading transportation platforms. You'll work with rich, complex transportation data - from ride bookings and payments to real-time operational signals - designing the pipelines and systems that turn raw data into clean, reliable, and actionable insight across the business. This is a high-ownership role on a talented and deeply motivated engineering team in Tel Aviv, where your work will directly shape how our company understands and improves its service for cities and riders around the world.
About the Role:
Design and build highly scalable, reliable data pipelines that serve clean, structured data across our company's engineering, product, and business teams - ensuring the entire organisation can trust the data it works with
Own the architecture of complex data models that translate messy, real-world transportation data - bookings, payments, driver activity, and more - into systems that are fast, efficient, and built to scale
Lead end-to-end development across the full data lifecycle: from architecture and design through to deployment, monitoring, and continuous improvement
Proactively monitor data quality and reliability, identifying and resolving discrepancies before they reach stakeholders - building the kind of data infrastructure people can depend on
Collaborate with a broad forum of engineers, analysts, and business stakeholders to identify data needs, design POCs, and ship scalable solutions that make a real difference to how our company operates
Contribute to the adoption and evolution of our modern data stack, including DBT, Airflow, Iceberg storage, and AWS big data tooling such as Glue, EMR, and Athena.

We are looking for a Data Products Engineer to take full ownership of our internal product suite - the full-stack applications, AI agent tools, and automation pipelines that power teams across the entire company every day. You will step into an established ecosystem and take the reins. Your mission is to maintain, evolve, and build the next generation of these internal tools so every department can move faster and make smarter, data-driven decisions.

Reporting directly to the VP of CS & Data, this is a high-autonomy, tech-lead role. You wont just write code; you will make architectural decisions, solve complex domain challenges with smart software, and shape how the entire company interacts with data.

How will your day-to-day look?
Own the Internal Product Suite: Lead the development of our core full-stack applications. You will build and scale the central platforms and dashboards that give Product, CS, Sales, and executive leadership visibility into product usage, account health, business performance and more.
Advance the Internal AI Layer: Design, deploy, and refine custom agentic AI workflows - enabling teams across the organization to query complex B2B and product data naturally and conversationally.
Engineer Smart Automations: Architect workflow automations and custom APIs that pipe downstream data directly into the operational tools our teams use every day.
Bridge Data and Software: Partner seamlessly with Data Ops and Data Analysts to transform raw data models into highly usable, intuitive user interfaces and automated actions.
Run the Infrastructure: Own the end-to-end DevOps lifecycle for our internal applications, managing deployment, containerization, observability, and CI/CD pipelines.
Drive Technical Strategy: Act as the technical anchor for internal tools. You will translate high-level product and business challenges from company leaders into shipped, reliable software.

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.

We're looking for an experienced Software Engineer to join our Data Platform Group. In this key role, you will build the company data platform: cloud-based microservices and data pipelines that process on the order of 1M records/sec at low latency. Because the platform is the foundation other groups build on, your work has a direct impact on our customers and enables engineering, product, and research teams across the organization.
The Lakehouse team owns the data itself - how it is stored, organized, retained, and served. We own our analytical storage layer, the batch processing built on top of it, and the APIs through which customers and the rest of the company consume data. If you enjoy the problems that only appear at petabyte scale - physical data layout, query performance, storage cost, and retention - this is the role.
Responsibilities:
End-to-end ownership of our large-scale analytical storage layer: data modeling, schema and table design, partitioning, retention, and query performance.
Design and develop the batch processing layer over our data lake using Spark on EMR (Java and PySpark).
Build and evolve Java/Spring Boot services that expose our data through well-defined APIs to customers and to consumers across the company.
Own performance and cost: query optimization, file layout and compaction, cluster sizing, and storage efficiency at scale.
Research new technologies in the lakehouse and analytical-storage space and adapt them for use in our product.
Work closely with product, DevOps, and security teams.

We are looking for a talented Engineer to join one of the most strategic and impactful teams in the company.

Our Integration team is the backbone of the organization, connecting all security ecosystems into one intelligent platform. The team plays a critical role in enabling everything we do, and its impact is felt across the entire business.

In this role, you will build and manage integrations with enterprise environments, security platforms, cloud infrastructures, and third-party systems. You will work on complex technical challenges involving APIs, data flows, automation, scalability, and system connectivity.

This is a highly cross-functional position with direct collaboration across R&D, Product, Customer Success, and Solution Engineering teams.

What you will do

Design, implement, and maintain integrations between multiple products and systems via APIs.

Solve complex data and scalability challenges, ensuring the platform efficiently handles large volumes of data.

Collaborate with cross-functional teams to understand requirements and deliver integration solutions that meet business needs.

Define best practices for data handling, API usage, and integration workflows.

Ensure the integrity, reliability, and performance of integrated systems.

In this role, you will be dedicated to safeguarding our clients data within the dynamic SaaS landscape. Joining our core Data Protection engineering group, you will drive innovation in SaaS Data Security, building the next generation of an enterprise SaaS data protection platform.
In the SaaS Data Security domain, you will develop comprehensive data discovery, classification, and protection solutions natively integrated with leading business applications (such as Microsoft 365, Google Workspace, Slack, and Salesforce). You will tackle complex performance and scale challenges, architecting high-throughput, multi-tenant systems capable of continuously scanning, processing, and accurately classifying high-volume unstructured data across diverse SaaS ecosystems.
You will solve complex architectural challenges-translating conflicting data models and normalizing complex, vendor-specific APIs-to build a single, cohesive security control plane. Your work will enable deep visibility, real-time activity monitoring, and automated remediation to eliminate security blind spots, configuration drift, and unauthorized data exposure across the entire SaaS footprint.
Ultimately, your work will focus on proactively identifying and eliminating risks, ensuring our clients' sensitive information remains secure, compliant, and accessible only to authorized users, empowering them to scale their operations safely.
Key Responsibilities
Drive innovation by designing and implementing impactful solutions that address client needs, contributing to the full feature development lifecycle from design to deployment.
Take ownership of specific feature segments, ensuring high-quality code and robust functionality through meticulous attention to detail and a focus on execution.
Proactively collaborate and exchange information with cross-functional teams, including product and infrastructure, to ensure seamless integration and alignment on shared objectives.
Challenge the status quo by generating innovative ideas and actively participating in brainstorming sessions to foster product and architectural improvements.
Actively engage in technical discussions, openly sharing knowledge and learning from others to solve complex problems and elevate team expertise.
Design and build highly scalable, resilient, and secure cloud-based applications and microservices.
Contribute to an agile and dynamic engineering culture, demonstrating a strong drive and outstanding communication skills to deliver results efficiently.

we are looking for a *DevOps Engineer.
What you'll do:
Own Multi-Cloud Infrastructure: Work alongside the team to design, scale, and operate our high-scale, multi-region production infrastructure across AWS and GCP, powering construction sites globally.
Drive Kubernetes at Scale: Manage and evolve our Kubernetes platform on EKS and leveraging GitOps practices with ArgoCD and Helm to enable safe, fast, and reliable deployments.
Build Robust CI/CD: Design and maintain CI/CD pipelines that empower dozens of engineers to ship confidently - with automation, testing, and progressive delivery built in.
Tackle Diverse Infrastructure Challenges: Work hands-on with a wide variety of workloads - from heavy data processing and Computer Vision pipelines to backend services and ML inference - each with unique scaling, performance, and reliability requirements.
Ensure Reliability & Observability: Build and maintain world-class observability (metrics, logs, tracing, alerting) so that issues are caught early and resolved fast. Performance, reliability, and scalability are at the core of what you do.
Security & Cost: Partner with the team to strengthen our security posture, identity and access management, compliance, and cloud cost optimization across both clouds.
Ownership from 0 to 1: You will have real influence over our architecture and tooling. We want engineers who care about shaping what we build and how we build it, ensuring performance, security, and observability are baked in from day one.