Top Tech Jobs & Startup Jobs

Owns end-to-end project management for Academy content development initiatives. Responsibilities include creating plans, milestones, and execution rhythms; coordinating cross-functional teams and subject-matter experts; tracking dependencies, risks, capacity, and deliverables; maintaining dashboards and documentation; facilitating status meetings; escalating blockers; and improving project governance and delivery predictability. This is an individual contributor role requiring accountability and influence without direct authority.

Build and operate Cognite’s serverless execution engine and workflow orchestration platform. Responsibilities include designing distributed, multi-tenant systems; owning reliability, scalability, APIs, observability, CI/CD, testing, and performance; and supporting ML workloads, workflow orchestration, data storage, and event-driven architectures across multiple clouds. The role also involves optimizing costs and enabling industrial AI and contextualization pipelines.

Lead end-to-end delivery of complex Industrial AI and digital transformation projects, managing scope, schedules, budgets, risks, financial performance, governance, customer outcomes, adoption, and value realization. Collaborate with technical teams and executive stakeholders, facilitate steering committees and value workshops, support change management and training, and improve delivery practices. The role requires enterprise technology implementation experience, technical fluency, industrial-sector familiarity, strong commercial skills, French fluency, and travel of up to 25%.

Own and architect production-grade data pipelines integrating SAP/ERP, logistics platforms, and market intelligence sources into Cognite Data Fusion. Design supply chain data models and knowledge graphs optimized for AI-agent traversal, build Python and SQL ETL using REST APIs, and improve packaged integrations. Lead customer discovery and technical workshops, collaborate with product, delivery, and technology partners, and support rollouts. Ensure scalable, maintainable, well-documented pipelines aligned with supply chain standards and customer requirements.

Lead production systems for Context Services, translating ML, deep learning, and LLM solutions into reliable software services. Design scalable, low-latency APIs and architectures; build model-serving, vector database, and agentic workflow frameworks; manage state and prompt tracking; integrate with platform infrastructure; and collaborate with product, architecture, and AI teams.

Own the technical architecture for Context Services, designing fault-tolerant, scalable AI and software systems for high-concurrency industrial applications. Lead model serving, vector databases, LLM and agentic orchestration, platform integration, and production reliability. Translate ambiguous business and data problems into technical strategy, guide architecture decisions, establish engineering standards, mentor engineers, and lead complex initiatives from conception through deployment. Drive innovation through research, prototyping, and relevant open-source contributions.

Own strategic relationships with high-value global enterprise accounts, driving account growth, value realization, executive engagement, complex negotiations, and accurate forecasting. Lead cross-functional teams, apply Command the Message and MEDDPICC sales methodologies, build business cases, and sell SaaS, cloud, big data, and generative AI solutions to Oil & Gas, Refining, and Chemicals customers. The role requires French and English fluency and approximately 50% on-site travel.

Develop and deploy data extractors, integrations, data models, ingestion pipelines, visualizations, and AI agents for industrial clients. Design solutions using Python, SQL, Spark, cloud platforms, Kafka, Docker, and Cognite Data Fusion. Collaborate with customers, SMEs, product teams, and industrial stakeholders to translate requirements into scalable architectures and product capabilities. Ensure data contextualization, security, CI/CD, monitoring, logging, operational support, and technical quality while advising customers and partners.

Lead scalable data engineering solutions using Cognite Data Fusion, including integrations, data extraction, modeling, cleansing, pipelines, and analysis. Collaborate with data scientists, architects, project managers, engineering, and product teams to deliver customer solutions. Conduct code reviews, support customers and partners, contribute to Cognite tools and SDKs, and apply DevOps practices across cloud and distributed computing environments.

Own the reliability, scalability, automation, and operations of PostgreSQL, Elasticsearch, and Kafka infrastructure across multi-cloud Kubernetes environments. Responsibilities include fleet orchestration, provisioning, upgrades, backups, monitoring, disaster recovery, capacity planning, performance tuning, incident response, and infrastructure automation. The role partners with engineering and platform teams to reduce operational toil and deliver resilient, self-service database infrastructure.