Location: LATAMWork Model: 100% RemoteProject: Identity Security / Cybersecurity PlatformSeniority: SeniorEngagement: Full-time143L - Senior Data Engineer (Python/Go/Data Ingestion/Cloud) · Remote · LATAMLocation: LATAMWork Model: 100% RemoteProject: Identity Security / Cybersecurity PlatformSeniority: SeniorEnglish Level: B2 / C1Engagement: Full-timeGet to Know DarwoftAt Darwoft, we build custom software solutions and partner with international companies to create high-impact digital products.We are a Latin American technology company with a people-first culture, strong professional standards, and a long-term partnership mindset.
We work with distributed teams, complex technical challenges, and clients whose technology has a direct impact on their businesses and users.About the ProjectWe are partnering with an innovative cybersecurity company building a next-generation Identity Security and Identity Observability platform .
The product helps enterprise security teams understand how human users, service accounts, workloads, applications, and AI-driven identities access systems across cloud, SaaS, network, endpoint, and on-premise environments .
At the core of the platform is a highly data-intensive architecture that collects and analyzes large volumes of events, logs, network telemetry, identity activity, and security signals .
This information is processed to identify unusual access patterns, security risks, identity blind spots, infrastructure misconfigurations, and potential threats.The platform integrates with complex enterprise ecosystems including identity providers, cloud platforms, SASE solutions, SIEMs, endpoint security tools, network infrastructure, and security services.About the RoleWe're looking for a Senior Data Engineer focused on Data Ingestion, Integrations,



and Collectors to become a key technical bridge between enterprise customers and the engineering organization.This is not a traditional backend or internal data engineering position.You'll work directly with customers to understand their infrastructure, data sources, security architecture, network topology, APIs, and workflows, then design and implement scalable mechanisms to ingest that information into the platform.You'll combine data engineering, software engineering, solution architecture, cloud infrastructure, and technical consulting .
A major part of your mission will be building and evolving connectors and ingestion pipelines capable of integrating data from complex enterprise environments while maintaining reliability, scalability, security, and fast time-to-production.What You'll Be DoingDesign, develop, and maintain production-grade data collectors, connectors, and ingestion services .
Integrate enterprise data sources including identity providers, cloud platforms, SASE solutions, network infrastructure, endpoints, applications, security platforms, and SIEM systems .
Work directly with enterprise customers to understand their environments, data flows, APIs, security requirements, and integration constraints.Participate in technical onboarding processes, from initial discovery and architecture design through production deployment.Design scalable ingestion architectures for processing security events, logs, identity activity, network telemetry, and other high-volume data sources .




Build integrations using REST APIs, cloud-native services, event-driven architectures, and modern software engineering practices .
Develop backend services primarily using Python and/or Go .
Work with SQL and NoSQL databases to store, transform, query, and analyze high-volume data.Build and operate containerized services using Docker and Kubernetes .
Deploy and integrate solutions across Ature.Develop rigorous evaluation suites and diagnostic tools to assess model readiness for deployment.Strengthen feedback loops from real‐world product usage, incorporating both explicit and implicit user signals into post‐training.Collaborate with tooling, product, and training teams to improve the usefulness, reliability, and agentic capabilities of frontier models.Closely liaise with research, engineering and cross‐functional teams to determine which integrations are production‐ready for inclusion in major model releases.Degree in Computer Science, Machine Learning, or a related field; advanced degree (MS/PhD) preferred with a strong publication record in top‐tier AI conferences.Experience with multimodal post‐training workflows and data pipelines, particularly for agentic systems and tool use.Hands‐on experience applying post‐training at scale using distributed training frameworks (e.g., multi‐node GPU environments).
Demonstrated experience improving model capabilities in areas such as reasoning, tool use, and multi‐agent coordination that achieve SOTA results.Proven track record of open‐source contributions related to agentic systems or tool use (code, datasets, or models) on platforms such as GitHub or Hugging Face.Publications at leading AI conferences (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, ECCV).
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