Job Description

The Budget Steering squad builds a holistic approach to budget allocation and optimization for performance marketing — from quarterly planning to monthly optimization down to daily decisions at campaign and ad-group level.

We operate in a highly complex and ambiguous environment where:

- Ground truth is often unobservable (incrementality vs attribution)

- Budget decisions must be made continuously, under uncertainty and with imperfect data

- Every allocation change moves real spend across markets, channels and platforms

Our goal is simple to state and hard to solve: invest each euro where it generates the biggest leverage , across scenarios ranging from volume maximization to payback-based allocation.

We are looking for an Engineering Manager to lead a cross-functional squad of engineers and data scientists , own a large-scale production service the business depends on daily, and turn ambiguous commercial questions into reliable, model-driven decision products . This role requires a strong technical foundation — in backend engineering or in data science / ML — combined with the leadership to grow people and the pragmatism to partner closely with Product and business stakeholders.

In this role you will,

Lead and grow a cross-functional squad

- Lead engineers and data scientists across the full product lifecycle — from problem framing to production and adoption

- Grow individuals through coaching, feedback and clear development paths, and hire to close capability gaps

- Set measurable goals for the team and create the conditions for the squad to hit them

- Build a culture of ownership, proactiveness and high engineering standards

Own the budgeting lifecycle end to end

- Take accountability for budget allocation across every dimension and timeline — from strategic quarterly allocation to daily bid and budget changes at campaign level

- Drive the development of payback-based allocation models , including CLV estimation and constrained optimization

- Consolidate a fragmented tool stack into a single budgeting platform with coherent interfaces, ownership and roadmap





- Balance methodological rigor with business constraints — budget caps, pacing and channel dependencies

Guide technical strategy and execution

- Shape the squad's technical strategy and stay close enough to the work to be a credible partner in most technical conversations at squad and domain level

- Make architecture and prioritization trade-offs explicit — effort, value, feasibility and pace

- Set the bar for reliability and data quality : uptime, correctness of allocation, and validated inputs and outputs across every source

- Ensure solutions are built as decision products , not one-off analyses — tested, monitored, documented and reproducible

Partner with Product and business stakeholders

- Work with Product Managers to define scope, trade-offs and timelines, and to set common, measurable goals across planning cycles

- Act as an ally of Product: negotiate constructively, know when and how to say no, and compromise without losing the long-term technical direction

- Reduce ambiguity by asking the right questions before committing the team to build

- Navigate a complex stakeholder network spanning marketing, finance, analytics and central platform teams

Drive decision-making under uncertainty

- Provide clear recommendations despite imperfect measurement , articulating trade-offs and confidence levels

- Navigate conflicting signals (, attribution vs incrementality vs MMM) and help the team decide what to trust

- Translate complex modeling and system behaviour into clear narratives for non-technical audiences

- Build trust in the squad's outputs over time, including when they contradict established assumptions

Qualifications

- Technical Excellence:



Depth in either of the following, with hands-on experience on cross-functional products:

Software Engineering: strong backend background, building scalable and reliable applications used in production every day

ML / MLOps or Data Science: deep expertise across the ML lifecycle and its tooling ecosystem

Willingness and ability to ramp up on the other side quickly: the squad is cross-functional by design, and you will be expected to engage credibly with both.

- People Leadership: Proven track record leading and mentoring engineers and/or data scientists, growing individuals and taking accountability for a large-scale production service. Ability to mediate and resolve conflict without taking sides, acknowledging different perspectives and building shared understanding.
- Building Systems Where Modelling Is the Core: Experience delivering scalable, reliable applications with data science modelling at their centre — where the model output is the product, not a supporting artefact.
- Comfort with production-grade practices: modular design, testing, CI/CD, monitoring, version control and modern data/ML stacks.
- Product Collaboration & Business Acumen: Pragmatic and realistic. Evaluates business value against technical feasibility, and turns complex business requirements into bite-sized, deliverable work for the team. Understands which metrics matter and what a marketplace business optimizes for — or can demonstrably follow the train of thought and reach sound conclusions in an unfamiliar domain.
- Innovation & Ownership: Experience building new solutions from scratch and setting measurable goals for a team. Deep sense of ownership and proactiveness — comfortable operating without a clearly defined problem statement.

Nice to have:

- MarTech Domain Expertise: Understanding of performance marketing: budget allocation and optimization, bidding, attribution, and the dynamics of incrementality, diminishing returns and cross-channel interactions .
- Experience building bidders or budget optimization systems is a strong plus.

📌 Manager, Software Engineer (Barcelona)
🏢 Glovo
📍 Barcelona