Engineering Team Lead (Geospatial Analytics)

Role highlights:

• Engineering Team Lead (Geospatial Analytics)
• Location: Espoo, Finland. Relocation to Finland required
• Department: Product Engineering, Solutions
• Reports to: Engineering Manager, Geospatial Product Engineering
• Employment type: Permanent
• Workplace model: Hybrid
• Employment is subject to applicable security screening (incl. SUPO, where required)

Why this role matters:

As Engineering Team Lead (Geospatial Analytics), you'll own a product engineering team that turns remote sensing and auxiliary data into fast, reliable insight when natural catastrophes happen. The team currently supports our Wind, Earthquake and Landslide products and brings together analytics (SAR, Machine learning / Data Science) and software engineers.

You will work in tandem with the Product Manager to decide what to build and why, and closely with Operations to make sure the products perform during live events.Your decisions shape how several products work, so you'll own the product’s engineering domain end to end: analytics that serve several products at once, built to the level the problem needs and no further. You will be hands-on, which keeps your technical judgement credible and your feedback specific.

Your day-to-day responsibilities

• Lead and own the product engineering team: hiring, growth, performance and wellbeing of the team members
• Own the team's technical direction across analytics and engineering, taking work from research prototype to production systems that run reliably against customer commitments.
• Work in tandem with the Product Manager on roadmap and priorities, turning customer needs into analytics capability.
• Design shared change detection analytics that serve multiple products in the team’s portfolio
• Collaborate closely with Operations on event response, so that delivery workflows, latency and quality hold up when it matters.
• Contribute directly to analytical development (SAR, geospatial data science / ML), validation and code.
• Set and maintain scientific and engineering standards across the team, including review practice, validation rigour, testing and documentation.
• Stakeholder management - including Product Engineering leadership and different product stakeholders

What we’re looking for

Must haves:

• Master’s or PhD in Remote Sensing, Machine Learning, Data Science, Electrical Engineering, Geoscience, Physics, Computer Science or a related field, or equivalent experience.
• Experience in hands-on leadership as a line manager or tech lead, with a track record of delivery and examples of people who grew under your leadership.
• Experience working closely with product and operations teams in a flow-based, outcome-focused way, including giving direct feedback constructively and explaining technical trade-offs to non-technical stakeholders.
• A record of taking analytics from prototype to reliable production against latency, accuracy and uptime targets, with sound judgement on when a solution is good enough.
• Experience building, validating and deploying ML models or analytical pipelines based on SAR or other remote sensing imagery for tasks such as damage assessment, change detection or object detection. This includes working with noisy or sparse labels, validating against ground truth such as field surveys or claims data, and monitoring model performance in production.
• A solid grasp of SAR principles, including how backscatter responds to structural change in built environments, and applied experience in change detection using amplitude, coherence or interferometric (InSAR) methods.
• Strong Python skills and experience applying modern engineering practice (Git, code review, testing, CI, documentation), deployed with Docker on AWS. Ability to lead cross-functional teams.

Nice to haves:

• Experience fusing remote sensing imagery with auxiliary data, such as weather, terrain models or building attributes, to improve model performance.
• Experience using geospatial foundation models and embeddings (e.g. AlphaEarth Foundations) for tasks such as classification, similarity search or change detection.
• Experience with natural hazards, disaster response, insurance or emergency management use cases.
• Hands-on use of AI-assisted development tools in your daily engineering work.
• A publication or patent record, or other evidence of sustained technical contribution.

Application Process

• TA Interview
• Hiring Manager Interview
• Take Home Task
• Panel Interview
• Department Lead Interview
• Final Interview with SVP of Solutions

How We Work (Our Values)

• Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions.
• See the big picture: Stay close to what’s happening across the company so you can make better decisions. Consider how your work affects others.
• Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions.
• Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift.
• Have fun: What we do matters—and it should be enjoyable. Celebrate progress, take pride in results, and share the wins.