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Guideline Group is a leading technology group in Israel, operating across FinTech, InvestTech, LegalTech, and BusinessTech. Techdin, part of Guideline Group, is looking for a Senior Product Manager to join our LegalTech product team and lead the development of complex, AI-driven products. We’re looking for a curious, hands-on Product Manager who knows how to take a complex customer or business need, ask the right questions, distill what really matters, and turn it into a clear product solution that can be built and delivered. Someone who can look at a problem from different angles, explore and validate possible solutions, make informed trade-offs, and turn the chosen direction into a clear plan for execution. What you’ll do
* Own product initiatives from discovery through delivery.
* Understand customer and business needs, challenge requirements and assumptions, and identify the real problem behind the request.
* Explore and validate different solution approaches before deciding what and how to build.
* Turn the chosen solution into clear features, user flows, business logic, requirements, and development-ready specifications.
* Work closely with R&D and Data to assess feasibility and effort, refine scope, and make informed product and technical trade-offs.
* Prioritize based on customer value, business impact, effort, and technical complexity.
* Drive execution from planning through development and successful delivery.

Responsibilities:
Consulting for FrontEnd best practices & tools.
Transforming orgs from monoliths to micro-frontend architecture.
Establishing coding standards across multiple development teams.
Creating tech design documents from a feature descriptions.
Implementing with performance, security and scalability in mind.
Utilizing industry standard paradigms.
Taking feature ownership from design, architecture, task breakdown, implementation and testing and production.
Always Hands-on.

Responsibilities:
Microservices Transformation- Lead the migration from monolithic systems to microservices using Kubernetes, Istio, and Linkerd. Your work will focus on ensuring reliability, scalability, and fault tolerance in production.
Container Orchestration- Manage Kubernetes clusters at scale, automate deployments with Helm, and integrate continuous delivery using Gitops technologies (ArgoCD).
Developer Experience- Improve CI/CD pipelines with GitLab CI, github-actions, and other tools to enhance productivity and streamline workflows.
GitOps and Automation- Implement GitOps using FluxCD and automate infrastructure management with Terraform. Youll be responsible for maintaining security and configuration management with HashiCorp Vault.
Monitoring and Observability- Build and manage observability stacks using Prometheus, Grafana, Loki, and Jaeger, ensuring full visibility and fast incident response.
Production Readiness- Define production-readiness standards and implement monitoring tools like Datadog and New Relic to ensure systems are stable and performant.

In your role as a Data Engineer at Tikal, you'll be actively engaged in the most fascinating and demanding projects for our clients. Handling high-scale operations, heavy traffic, stringent SLAs, and massive data volumes is just the starting point. Our leadership in the realms of Big Data, Streaming Data, and Complex Event Processing (CEP) stems from our adept use of cutting-edge, optimal technologies.

Responsibilities:
Apply software-engineering methods and best practices (Medallion Architecture) in Data Lake management, using tools like DBT.
Develop Data pipelines to ingest and transform data.
Manage and enhance orchestrated pipelines across company
Data Modeling: Developing data models to ensure efficient storage, retrieval, and processing of data, often involving schema design for NoSQL databases, data lakes, and data warehouses. This will also include catalogs of data.
Scalability: Ensuring that the big data architecture can scale horizontally to handle large volumes of data and high traffic loads.
Data Security: Implementing security measures to protect sensitive data, including encryption, access controls, and data masking.
Data Governance: Establishing data governance policies to ensure data quality, compliance with regulations, and data lineage.