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Data Engineer - DMP, Streaming & AnalyticsYassir · Algiers, Algeria
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Yassir

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Data Engineer - DMP, Streaming & Analytics

🗓️ Posted 2026-09-10 Algiers, Algeria Full-time Hybrid ICT
💰 Salary Not shared 📊 Market NA

About the Company

Context & Vision In 2026, writing code is no longer the primary bottleneck; managing its complexity and ensuring its reliability is. We are building a highly resilient advertising platform with a very lean, senior internal team, and we intend to keep it that way. To achieve scale without the overhead of a large engineering department, we rely on an AI-first development paradigm and a philosophy borrowed from the best large-scale open-source projects. Our code is not public, but our governance is theirs: asynchronous communication, exhaustive written documentation, explicit rules, and uncompromising quality gates. It is the only way a small team of humans and AI agents ships serious systems without accumulating technical debt. This role owns the data engine underneath the platform: the streams, segments and audiences that make advertising intelligent.

Responsibilities

  • ● he segment and audience computation, and the analytical stores that feed targeting and reporting. What we hire for is judgment: the problems here are hard in the interesting way, and the interesting part is the tradeoffs you choose.
  • ● Hard problems, at real-time scale.Computing audiences and measurement over very large, high-velocity event streams, in near-real-time, where being right is not always the same as being exact and every choice has a real cost. We describe the problems; we expect you to bring the approach.
  • ● Stream-First Pipeline.Own the ingestion and processing pipeline. It is stream-first by design; you keep it that way rather than falling back to batch.
  • ● Segments & Audiences.Own how raw signals become targetable audiences - how they are computed, stored, refreshed, and exposed to the serving path. This is the intelligence layer of the product.
  • ● AI-First Delivery & Governance. We expect AI to write most of the code; you are the editor, not the typist. You specify work as explicit, self-sufficient issues, direct agentic tooling to implement, and gatekeep every diff. Schemas, data contracts and Architecture Decision Records are documented. If it is not written down, it does not exist.
  • ● The Tech Environment
  • ● Data: event streaming and stream processing, relational and analytical stores.
  • ● Language: the codebase is a compiled systems language; we care more about how you reason about data than about the language you arrive with.
  • ● Domain: DMP, segments, audiences, identity, measurement.
  • ● Cloud: Google Cloud Platform, Kubernetes.
  • ● Profile & Requirements

Requirements

  • ● + years building data or backend systems in production, with genuine ownership of a pipeline's reliability and the correctness of its output.
  • ● Depth on hard data problems- you have built things where scale, real time, or accuracy made the design non-obvious, and you can talk through the tradeoffs you chose and why.
  • ● Streaming experience and a real grasp of stream-vs-batch tradeoffs, event modelling, and schema evolution.
  • ● Comfort in a compiled systems language (Rust, Go, C++, Scala or similar). The codebase is Rust; the language is the tool, the data thinking is the skill.
  • ● Working knowledge of an analytical data store and SQL. DMP / ad-data or audience/segmentation experience is a strong plus.
  • ● Core Competencies & Mindset
  • ● Judgment over recipes: you frame a data problem and its constraints before choosing an approach, and you can say when an approximate answer is the right engineering call and when it is not.
  • ● Distributed & complex systems: knowledge of, and a taste for, distributed and complex systems is very welcome. Tell us about the ones you have built or operated - what made them hard.
  • ● AI Development Lifecycle: genuine proficiency editing and validating AI-produced code, and decomposing ambiguous goals into specifications an agent can execute unattended.
  • ● Uncompromising Quality: a maniacal focus on data correctness, freshness, and reliability. A wrong number that ships is a wrong number the business trusts.
  • ● Written & Asynchronous: exceptional written communication; effective in a distributed, async environment (Paris timezone +/- 3h).
  • ● Ownership: you own your pipeline and its documentation completely, and you surface problems before they are asked about.