דרושים » אבטחת מידע וסייבר » Senior SecOps Engineer

It starts with you - an engineer driven to build resilient, automated infrastructure that enables teams to move fast with confidence. You care about operational excellence, developer experience, and reliability at scale. Youll architect and operate the compute and networking infrastructure that powers our AI platform - from CI/CD pipelines to Kubernetes clusters to observability systems - across cloud and on-prem environments.
If you want to build infrastructure that powers mission-critical AI systems at national scale, join mission - this role is for you.
:Responsibilities
Architect and operate Kubernetes-based infrastructure across AWS and on-prem environments, ensuring high availability, security, and performance.
Design and maintain CI/CD pipelines for application and service deployments with automated testing, security scanning, and rollback capabilities.
Drive infrastructure-as-code practices for compute and networking - building reproducible, auditable, and version-controlled infrastructure.
Own reliability and incident response - establish SLOs, build alerting systems, lead incident resolution, and drive post-incident improvements.
Enable AI-native operations - support agentic deployment pipelines, self-healing infrastructure, and secure sandboxing for model experimentation.
Build and maintain observability systems - metrics, logging, tracing, and dashboards that provide visibility into system health.
Optimize infrastructure cost and performance - right-size resources, implement auto-scaling, and identify efficiency opportunities.
Collaborate with Engineering, Data Platform, Data Engineering, and Security teams to align infrastructure with platform needs.
Shape infrastructure characteristics that support data freshness, correctness, and low-latency pathways for AI training/inference, retrieval, and agentic workflows.
Contribute paved-road tooling - reusable CI/CD patterns for services, IaC modules for compute and networking, and runbooks - that streamline delivery across teams.
Collaborate with Engineering, Data Platform, Data Engineering, Security, Product, AI/ML, Data Science, and Analytics to anticipate and meet cross-functional needs.

It starts with you - an engineer driven to operate database systems at the highest level of reliability and performance. You care about query latency, uptime, data durability, and getting paged as little as possible. Youll operate, tune, and scale the database engines that serve the platform - from PostgreSQL to Elasticsearch, Redis to vector databases, across cloud and on-prem environments.
If you want to run the databases that power mission-critical AI at national scale, join Dreams mission - this role is for you.
The Dream-Maker Responsibilities
Operate and maintain database systems - PostgreSQL, Elasticsearch, Redis, MongoDB, vector databases, and others across cloud and on-prem.
Own database reliability - high availability configurations, replication, failover automation, and SLA adherence.
Drive performance tuning - query optimization, index design, configuration tuning, and resource profiling to meet latency and throughput targets.
Execute operational procedures - backup/recovery, disaster recovery testing, upgrades, migrations, and capacity scaling.
Lead incident response for database issues - troubleshooting production problems, root cause analysis, and implementing preventive measures.
Build monitoring and alerting - dashboards, metrics collection, slow query analysis, and proactive capacity alerts.
Enable new capabilities - deploying and tuning vector databases for AI workloads, evaluating new database technologies.
Collaborate with Data Platform, Data Engineering, Engineering, and Security teams on database operations and best practices.
Uphold database SLAs that support retrieval paths, feature stores, and embedding durability; coordinate safe schema evolution, partitioning, and replay/backfill practices.
Expose catalog and lineage signals - ownership, change history, and impact analysis - to improve trust and safe consumption for downstream users.
Collaborate with Data Platform, Data Engineering, Engineering, Security, Product, AI/ML, Data Science, and Analytics to balance performance, durability, and evolution across workloads.

If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities.
Job Description:
Responsibilities:
Provides guidance and leadership to design and develop reliable, cost-effective, and high-quality solutions for assigned systems portions or subsystems and advise on dealing with high-risk situations.
Identifies and evaluates new technology innovations to ensure alignment with the technology roadmap and business value; creates plans for integration and update into architecture.
Reviews and evaluates designs and project activities for compliance with development guidelines and standards; provides tangible feedback to improve product quality and mitigate failure risk.
Provides mentoring to others to set an example of software systems design and development innovation and excellence.
Analyzes science, engineering, business, and other data processing problems to develop and implement solutions to complex application problems, system administration issues, or network concerns.

It starts with you - an engineer who cares about building data pipelines and models that deliver reliable, trusted data. You value data quality, clean transformations, and making data accessible to those who need it. Youll work alongside experienced engineers to build ETL/ELT pipelines, maintain dimensional models, and implement quality checks that turn raw data into actionable intelligence.
If you want to grow your skills building data products for mission-critical AI, join mission - this role is for you.
:Responsibilities
Build and maintain ETL/ELT pipelines using platform tooling - workflows that extract from sources, apply transformations, and load into analytical stores.
Develop and maintain data models - fact/dimension tables, aggregations, and views that serve analytics and ML use cases.
Implement data quality checks - validation rules, tests, and monitoring for data freshness and accuracy.
Maintain documentation and lineage - keeping data catalogs current and helping consumers understand data sources and transformations.
Work with stakeholders to understand data requirements and implement requested data products.
Troubleshoot pipeline failures - investigating issues, fixing bugs, and improving reliability.
Write clean, tested, well-documented SQL and Python code.
Collaborate with Data Platform on tooling needs; work with Datastores on database requirements; partner with ML, Data Science, Analytics, Engineering, and Product teams on data needs.
Design retrieval-friendly data artifacts - RAG-supporting views, feature tables, and embedding pipelines - with attention to freshness and governance expectations.