Manager, Software Engineer

Hace 6 días

Barcelona, Cataluña, España Glovo Jornada completa
Overview

In this role you will lead a cross-functional squad that builds a scalable budgeting platform for performance marketing. You will transform ambiguous commercial questions into model-driven decision products, collaborating closely with Product and business stakeholders. The focus is on end-to-end budgeting—from strategic quarterly allocations to daily campaign decisions—delivering reliable, measurable impact. You drive payback-based allocation, CLV estimation, and data quality, shaping the squad’s technical direction. This is a high-impact role at scale in a fast-paced, cross-functional environment.

Compensaciones / Beneficios
  • private health insurance
  • Glovo credit for meals
  • Cobee discounts for transport and wellbeing
  • discounted gym memberships
  • flexible work from home two days a week
  • enhanced parental leave
Responsabilidades
  • Lead and grow a cross-functional squad of engineers and data scientists
  • Own the budgeting lifecycle end to end across quarterly to daily horizons
  • Drive payback-based allocation models including CLV estimation
  • Consolidate tool stack into a single budgeting platform with clear interfaces
  • Set goals for the team and foster ownership, high standards and proactive collaboration
  • Collaborate with Product and business stakeholders to align scope, timelines and trade-offs
  • Define and communicate technical strategy, architecture, and prioritization
  • Ensure reliability, data quality, and reproducibility of decision products
Requisitos principales
  • Strong backend software engineering background or ML/Data science with production-level experience
  • Proven people leadership of engineers and/or data scientists
  • Experience building scalable, production-grade systems with data science modelling at the core
  • Comfort with cross-functional collaboration and translating business needs into deliverables
  • Familiarity with CI/CD, testing, monitoring and modern data/ML stacks
  • strong communication with non-technical audiences
  • ability to negotiate and align stakeholders
  • ownership mindset
  • Backend development for scalable services
  • ML lifecycle and MLOps or data science tooling
  • Data quality assurance and reliable data pipelines