Software Engineering Manager

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.

• 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

• 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

• 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

• 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

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

• Navigate conflicting signals (e.g., 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

• 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.

• 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.