SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Cognite is a global SaaS pioneer using AI and data to solve complex industrial challenges. The company operates at the forefront of industrial digitalization, building AI and data solutions that drive operational improvements across energy, manufacturing, pharma, and chemical sectors. Cognite's core platform, Cognite Data Fusion (CDF), and AI agent workbench, Cognite Atlas AI, enable digital transformation for Fortune 500 industrial companies.
As a Senior Data Engineer in Tokyo, you will work with enterprise clients to design and implement end-to-end data solutions. Key responsibilities include: understanding client requirements and defining queries with subject matter experts; developing custom extractors using Python, Spark, and REST APIs; customizing existing extractors (SQL databases, Kafka event streaming) and deploying via Docker; creating custom data models for discovery, mapping, and cleansing; collaborating with product teams to translate customer needs into future features; prototyping data visualizations and dashboards; developing scripts and applications for data ingestion, transformation, and integration; designing AI agent solutions tailored to client needs; ensuring proper use of visualization tools (Power BI, Grafana, Plotly Dash) and transformation technologies (Databricks, Azure, GCP, AWS); modeling and contextualizing data efficiently within CDF using graphs and relational structures; designing comprehensive technical solutions ensuring fit within customer ecosystems; meeting quality standards (CI/CD, logging, security); defining clear operational responsibility and support contracts; designing integrations and data models using Cognite connectors, SQL, Python/Java, and REST APIs; and serving as a trusted technical advisor on Cognite Data Fusion.
Required qualifications: knowledge and enthusiasm for data engineering and AI; English proficiency (reading, writing, and conversational); Python coding experience; strong communication skills with clients and internal teams. Preferred qualifications include domain knowledge of operational data in heavy industries; bachelor's degree (master's preferred); consulting experience negotiating requirements; enterprise integration design experience; ability to lead data and analytics projects; broad technical knowledge of data and analytics stacks; BI tool expertise (Power BI); SQL and big data tools (Apache Spark); public cloud experience (AWS, GCP, Azure—Azure preferred) including networking, security, identity providers, and application hosting; operational support and change management experience; DevOps mindset with Git, CI/CD, and deployment experience; willingness to write code on advanced topics; data modeling and infrastructure architecture skills; familiarity with industrial equipment, data lakes, lakehouses, data warehouses, business intelligence, DataOps, and machine learning/generative AI; experience coordinating across diverse stakeholders from pilot to enterprise-wide rollout; and startup experience (Series A, B, or C).
