Data Engineering Leader

What Cognite is: Relentless to achieve Cognite operates at the forefront of industrial digitalization, building AI , and data solutions that solve the world’s hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements. We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not backwards - you’ll feel right at home here.

Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future.

About the role

Cognite's European Delivery organisation is growing in both scale and technical ambition. As Manager of Data

Engineering, you will lead, develop, and retain a team of Data Engineers operating across some of the most

complex industrial AI engagements in the market — ensuring they are technically strong, professionally

developed, and set up to deliver at the standard our customers expect. This is not a pure people management role. You will carry a meaningful delivery commitment — approximately

50% billable capacity — working on customer projects alongside your team. Your credibility as a manager

depends on being a practitioner: you need to understand the work at depth in order to coach it, assess it, and raise the bar on it. You will be part of the Value Delivery (VD) Europe profession leadership team and work closely with Portfolio

Managers, and cross-functional leaders across Value Delivery. How you’ll demonstrate Ownership People Development & Performance Own the professional development of your Data Engineers spanning junior to seasoned Data Engineers,

running structured 1:1s, calibrated performance reviews, and growth conversations grounded in the Data Engineering framework. Provide technically specific coaching — not generic management feedback. Your engineers should leave

conversations with a clearer understanding of what good looks like and how to close the gap. Identify and address skill gaps proactively, before they surface as delivery problems on customer

engagements. Build a team culture of ownership, technical rigour, and AI-first delivery — consistent with the behaviours

defined in the EDF at every level

Delivery Accountability Carry approximately 50% billable capacity on customer projects — this is a firm expectation, not a stretch

target. Review Statements of Work (SOWs) before your engineers are committed to them: assess scope fit,

identify skill gaps, and flag delivery risks before they become customer problems. Champion your team's interests in staffing decisions — actively match engineers to engagements where

they can deliver and grow, not just where there is a vacancy to fill. Act as an escalation point when delivery gets hard. Be the steady hand, not a passive observer. Profession & Team Building Contribute to standardising delivery practices across Value Delivery Europe — drive consistency in how we

design, build, and document data engineering solutions. Organise and lead professional development sessions relevant to the team's growth areas and project

landscape. Collect, prioritise, and escalate product feedback from your engineers and customers to the relevant

internal teams. Lead or contribute to hiring for your team — define what good looks like for each level and bring that

standard to interviews and onboarding. AI-First Leadership Model and reinforce AI-first delivery practices across your team — not as a policy, but as a standard of

work. Hold your engineers to the AI fluency expectations defined in the EDF at each level: daily use, responsible

validation, knowledge sharing, and continuous upskilling. Stay current on AI tooling relevant to industrial data engineering and bring that knowledge into your team's

practice.

What we are looking for Required Proven hands-on data engineering background — production-grade Python, SQL, REST APIs, and cloud-native pipeline development. You must be able to review your team's technical work and have credible

technical conversations with customers. At least one year of meaningful association with Cognite's platform and products, with direct delivery

experience on customer engagements. External candidates will not be considered for this role. Demonstrated ability to develop people — not just manage tasks. You have concrete examples of

engineers you have helped grow, with specific feedback that changed how they work. Customer-facing confidence: you can hold a technical conversation with a senior customer stakeholder,

translate engineering decisions into business language, and manage expectations under pressure. Strong operational judgement: you can read a SOW, spot a risk, and have a difficult conversation about

scope before it becomes a delivery failure.

Strong Advantage Formal or informal people management experience — direct reports, tech leads, or structured mentorship

programmes.

Deep Cognite Data Fusion expertise: data modelling, SDK development, AI/LLM integration patterns,

deployment packs. Experience working in a Value Delivery or professional services environment where you were

simultaneously accountable for delivery and team capability.

Familiarity with Cognite's EDF and comfort using it as a coaching and calibration tool.

Track record of hiring — you know what good looks like at different levels and you can articulate it clearly. How We Think About This Role The best candidate for this role is an E3 Data Engineer — or equivalent — who is ready to multiply their impact through others, not just through thei