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.

Who We Are

ICEYE is the world leader in sovereign intelligence from space. We deliver persistent monitoring capabilities to detect and respond to changes in any location on Earth.

ICEYE owns the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation. To our customers we provide intelligence with unmatched quality, latency and revisit times, in any weather, day or night. To governments who choose to operate their own constellation we provide this proven capability as a sovereign system.

ICEYE-built constellations serve customers in defence and intelligence, environmental monitoring, insurance and emergency management. We enable fast decisions that contribute to a safer future.

Founded and headquartered in Finland, ICEYE operates globally with over 1000 employees across Europe, North America, the Middle East, and Asia-Pacific.

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.