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Software Engineer - Peformance Analytics

Apple -
Paris (75)

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Détails de l'emploi

Il y a 15 heures

Qualifications

JavaScript
Sens de l'organisation
TypeScript
Python

Description complète du poste

The 3P Player Performance team (Paris) is a small, specialised team focused on the streaming performance of Apple's third-party apps, primarily Apple TV and Apple Music.
We sit at the intersection of player engineering and data science, acting as the bridge between streaming expertise and the broader engineering organisation.
Our work spans the full stack of performance improvement: understanding how video streaming behaves in the wild, running experiments, and turning telemetry into insights that drive real product decisions.
The team has built a solid data infrastructure: telemetry is flowing, pipelines are running, and dashboards exist.
But we've hit a new challenge, the data is rich but hard to consume. The dashboards are too complex for engineers outside the team, which means the perf team is still the bottleneck for any data-driven decision.
The next phase is a clear shift: from building infrastructure to delivering actionable intelligence directly to engineering teams.

Description
This hire is specifically about closing that gap. Not more pipelines, not more dashboards, but surfacing the right signal.
The goal is for any engineer in the 3P org to know, without asking the perf team, whether their latest release improved or degraded performance and what to do about it. They should also be able to drive experiments on their own.

Critically, this role requires someone with a proactive and consultative mindset. Many engineers in the org are not used to a data-driven approach and may not know what to ask for. A big part of the job is going beyond just responding to requests: identifying opportunities engineers haven't thought of yet, showing what's possible, and gradually bringing teams along toward a more data-informed way of working.

Preferred Qualifications
Has shipped tools used by other teams, not just internal analyses.
Consultative mindset - able to both listen and proactively surface opportunities that teammates didn't know they needed.
Experience making complex data accessible to non-expert users.
Comfortable working in both Python and JS/TS.
Background in application or streaming performance.

Minimum Qualifications
Solid foundation in data science, including statistics, A/B testing, regression detection, and performance analysis.
Comfortable with data engineering and able to work with existing pipelines as well as extend them.
Strong software engineering skills, with a track record of shipping real tools used by others - not just analyses.
Able to build clean, usable frontend interfaces and web apps on top of existing data infrastructure.
Proficient in JS/TypeScript, which is the core team stack.

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