Engineering Manager, Full Stack & Data
About the role
Role & Team
As an Engineering Manager, Full Stack & Data, you will lead across several cross-functional teams, managing up to ~10 full stack software engineers, data engineers, and GIS specialists. You will support teams building our customer-facing product as well as teams working on our data processing and pipelines to enable our ML-powered product.
Our product teams are cross functional and will typically include product managers, designers, engineers and subject matter experts relating to the team’s domain.
You will be accountable for the technical direction and delivery outcomes of your area. You will challenge your teams to pursue ambitious goals while providing a high level of support — growing engineering talent and fostering a highly collaborative, team-based environment where people can do great work.
Your primary focus will be on building high-performing teams and driving results through others. You will not be expected to spend significant time doing hands-on coding; however, you will be expected to dive deep technically when required and leverage your experience to support strong technical decision-making.
Time zone requirement: Europe (GMT/WET, CET) and Eastern North America (NST, AST, EST)
What You'll Do
- Enable our data engineers to become highly productive members of their cross-functional teams, empowering them to ship high-quality, reliable data platforms and pipelines on a daily basis.
- Grow the teams by attracting great talent from your network.
- Foster an inclusive and caring culture where everyone can do their best work.
- Provide regular 1:1 coaching and feedback to tap into the potential of all your team members and help them thrive and reach their aspirations.
- Be a strategic partner to product managers, machine learning engineers, and other stakeholders within your domains and ensure that we are making the correct data architecture and platform decisions that balance short and long term goals.
- Ensure suitability of the data architecture, pipelines, and data models within your domains to enable longer term product, analytics, and machine learning goals.
- Work closely with the other Product & Engineering leaders on strategy, technology and people, including improving our data engineering practices.