דרושים » תוכנה » Senior Data Infra Engineer - Big Data

Were looking for a Principal Backend & Data Engineer to own and evolve the core data-intensive backend architecture of our AI platform. This is a senior individual contributor role with broad architectural responsibility and deep hands-on impact.

You will be a technical authority in building scalable, high-performance backend and data systems that power our AI research engine - working closely with AI scientists, platform engineers, and product leaders. Beyond implementation, you will define technical direction, set engineering standards, and influence how we build and scale data-driven AI systems across the organization.

What Youll Do
Architecture & Technical Ownership

Own the architecture of core backend and data systems that support large-scale, AI-driven computation and inference.
Design and evolve high-performance analytical processing layers handling complex data workloads at scale.
Make and drive long-term architectural decisions across backend, data, and AI integration domains.
Define standards and best practices for performance, scalability, reliability, and data correctness.
Backend & Data Engineering

Design, implement, and optimize data-intensive backend services using Python.
Build and evolve analytical compute pipelines using technologies such as Polars, DuckDB, AirFlow, Iceberg, Spark and other ingestion and processing frameworks.
Collaborate closely with AI scientists to productionize algorithms and embed advanced intelligence into the platform.
Platform & Infrastructure

Architect and maintain containerized systems using Docker and Kubernetes.
Ensure systems are observable, debuggable, and operable in production environments.
Drive performance tuning and cost-efficiency across compute-heavy workloads.
Technical Leadership

Act as a technical authority and thought leader within the engineering organization.
Mentor senior engineers through design reviews, architectural guidance, and hands-on collaboration.
Serve as a force multiplier by unblocking complex technical challenges and raising the overall engineering bar.

We are seeking an experienced Data Science Team Lead to lead our data science and data engineering efforts and oversee a team of skilled data engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.

You will lead the team through architecture decisions, development, and deployment of mission-critical systems-while growing and mentoring a high-performing team.

Responsibilities

Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
Design, build, and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve data architecture in alignment with business and product goals.
Manage sprint planning, task breakdown, code reviews, and performance feedback for your team.
Contribute hands-on to key development tasks and architecture decisions.
Recruit, mentor, and grow the data science engineering team.

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.

CTO AI Lab Architect
About the Lab
We manage the software supply chain for 80% of the Fortune 100 - packages, container images, ML models, agent skills, MCP servers, and AI-generated code. The rules are changing fast, and we need someone who can build the next generation of tools to manage, govern, and secure it all.
Our CTO Lab is a small, senior team building what comes next. We sit across the entire platform - Artifactory, Xray, Curation, AppTrust, ML, AI Catalog, Fly, Runtime, Distribution - and our job is to figure out how AI changes all of it. We run focused experiments, prototype fast, demo often, and grow what works into products alongside our product and engineering groups.
This role has two modes. In Build mode, you'll work across the full breadth of the platform - from artifact management and security to ML lifecycle and developer experience. In Scout mode, you'll be our antenna - evaluating new AI frameworks as they drop, scanning for emerging patterns in agentic AI, supply chain attacks, and developer tooling, and feeding evidence into our strategic decisions.
As a CTO Lab Architect you will build:
AI-powered supply chain intelligence - LLM systems that reason over artifacts, dependencies, and release signals to move past static block/allow rules toward decisions a senior engineer would make.
Agent systems and governance - build and secure AI agents that operate against registries, pipelines, and deployment systems. Extend our controls to the AI artifact stack we ship today - MCP Registry, Agent Skills Registry, AI Catalog - and design the next generation of governance models.
AI woven across the platform - versioning, security, and provenance for AI artifacts, and capabilities that make developers faster wherever speed, trust, or judgment can be amplified.
Evaluation and measurement - benchmarks and pipelines that prove AI-powered approaches actually outperform traditional ones. Data beats opinions.
Technology scouting and signal analysis - evaluate new AI frameworks and security innovations as they emerge. Concise assessments of what's real, what's hype, and what it means for us.