Data Infrastructure Team Lead
Mosaic.tech
Other Engineering
Israel
HiBob helps modern, mid-size businesses transform the way they manage people, giving HR and managers all they need to connect, engage, develop, and retain top talent. Since 2015, we’ve achieved consecutive triple-digit year-over-year growth, all backed by our amazing team of Bobbers from across the globe, making us the choice HRIS of over ~5500 midsize and multinational companies.
Our HR platform is intuitive, data-driven, and built for the way people work today: globally, remotely, and collaboratively. Fast-growing companies across the globe such as Monzo, Happy Socks, Fiverr, and VaynerMedia rely upon Bob to help them create the best work experiences for their people.
Come and be you with us
Being a Bobber is all about being you. We want you to bring all parts of yourself to work, giving you the freedom and confidence to be the best you and do your best work. If that’s bubbly, shy, precise, funny, bold, kind, honest, brilliant, or anything in between, we’re waiting with open arms. Come join us.
We are seeking a Data Infrastructure Team Lead to join our Platform group. In this role, you will build and lead the team responsible for HiBob’s data infrastructure foundation - the systems that power our product experiences, trusted analytics, AI capabilities, and enterprise-scale operations.
Data infrastructure at HiBob is not just about moving data from one place to another. It is about building reliable, scalable, and secure infrastructure that helps product and engineering teams move faster, enables smarter customer-facing experiences, supports trusted business decisions, and creates the foundation for safe AI adoption across the company.
This is a leadership role for someone who stays close to the technology: setting direction, guiding architecture, reviewing designs, contributing to critical decisions, and working with the team through complex production and scaling challenges.
You will design and evolve scalable, reliable, secure, and AI-ready data systems, including orchestration, streaming, distributed storage, analytical infrastructure, metadata and governance capabilities, and permission-aware data access for sensitive HR data.
About the Platform group:
The Platform Group accelerates HiBob’s productivity by providing developers with tools, frameworks, and infrastructure services. We design, build, and operate critical systems that allow our platform to scale reliably, securely, and efficiently.
As part of this group, the Data Infrastructure team owns foundational capabilities that enable product teams, analytics teams, and AI-driven initiatives to use data safely and effectively. We focus on building reusable infrastructure, strong engineering practices, and scalable platforms that help the entire engineering organization move faster.
As the Data Infra Team Lead, you will help shape the technical foundation for HiBob’s next stage of growth, including enterprise-scale data systems, trusted analytics, and AI-ready infrastructure.
- Proven experience designing, building, and operating large-scale data infrastructure in production environments.
- Strong engineering fundamentals in databases, distributed systems, concurrency, storage, and reliability.
- Strong knowledge of analytical engines, columnar databases, and data warehouse/lakehouse architectures.
- Experience with data orchestration tools and distributed storage solutions.
- Understanding of data security, privacy, governance, lineage, auditability, and access control.
- Ability to make pragmatic architecture decisions, balance maitenance-vs-delivery tradeoffs, and optimize for scalability, reliability, simplicity, and cost.
- Proven ability to lead a team, mentor engineers, communicate clearly, and work effectively with cross-functional stakeholders.
- Experience building infrastructure for data pipelines, orchestration, streaming, analytical workloads, and large-scale data processing.
- Experience with observability, monitoring, alerting, incident management, SLOs, and production operations for data systems.
- Strong understanding of AI-ready data platforms, including metadata, semantic layers, retrieval patterns, data quality, governance, and permission-aware access to data.
- Expertise with relational databases; PostgreSQL experience is a plus.
- Hands-on experience with streaming systems such as Apache Kafka or equivalent technologies.
- Experience with cloud-based data infrastructure solutions, such as AWS or GCP.
- Familiarity with Kubernetes.
- Experience with modern lakehouse technologies, open table formats, metadata catalogs, or data governance platforms.
- Experience supporting AI, ML, LLM, semantic search, or agentic product use cases on top of production data.
- Experience building multi-tenant SaaS infrastructure, especially with sensitive or permission-heavy data.
- Experience with cost optimization and FinOps practices for data platforms.
- Experience creating internal developer platforms, self-service data tooling, or paved-road infrastructure for engineering teams.
- Develop, mentor, and lead a high-performing Data Infrastructure team, combining people leadership with deep technical involvement in architecture, design, delivery, and production operations.
- Own and evolve the data infrastructure foundation that powers HiBob’s product experiences, trusted analytics, AI capabilities, and enterprise-scale operations.
- Design, implement, and operate scalable, reliable, and secure data systems across orchestration, distributed storage, streaming infrastructure, analytical engines, metadata/catalog capabilities, and data serving layers.
- Build reusable, AI-ready data platform capabilities that enable teams to safely work with trusted company data, including sensitive HR data, permission-aware access, data quality, lineage, auditability, and clear ownership models.
- Partner with product, engineering, security, analytics, and AI-focused teams to turn business needs into pragmatic platform capabilities that improve product velocity, customer experience, enterprise readiness, and operational efficiency.