דרושים » ניהול ביניים » מנהל /ת פרויקטים ומנתח /ת מערכות בתחום הכרטוס החכם

Requirements:
Experience: 8+ years of hands-on experience in Data Engineering and Architecture, with a track record of building and shipping platforms at scale.
Experience with modern big data platforms such as Snowflake, Databricks, or similar technologies.
Hands-on experience with Data infrastructure experience (Orchestration, scalability, reliability, and cloud architecture).
Data Movement & Integration: Deep understanding of data movement strategies, including high-frequency batching, CDC, and real-time event streaming.
Technical Depth: Deep understanding of database internals. High proficiency in Python and SQL. You can dive into code when necessary to solve complex issues.
Modeling & Architecture: Strong know-how in dimensional modeling and schema design (relational and NoSQL), with proven experience implementing Data Warehouse or Lakehouse architectures.
GenAI & RAG Expertise: You have practical experience architecting and building RAG (Retrieval-Augmented Generation) pipelines, with specific knowledge of Vector Databases, Embedding Models, and LLM Orchestration frameworks.
Business Acumen: A strong ability to understand business objectives and translate them into technical strategies that drive tangible value.
Leadership and Communication: As this is a central role in the product tech organisation, you will need a strong ability to influence engineering teams and drive consensus without direct authority. You must have excellent communication skills to explain complex architectural concepts to C-level stakeholders and non-technical partners.

Requirements:
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.
Bonus Skills
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.