Analytics Software Engineer

Resilience Care; Lyon; Hybride; EUR 60 000 - 90 000; Plein temps

Résumé du poste

Resilience Care is building a robust Data & Analytics backbone to support clinical and research teams. You will implement backend features using Python/FastAPI, model data with dbt, and collaborate with researchers and clinicians to deliver reliable analytics.

You’ll contribute to a semantic layer for KPI governance, automate recurring tasks, and ensure data quality with tests and monitoring. This role combines backend and data engineering in a remote-friendly, hybrid setup.

Qualifications

• 3–5 years of data and software experience, typical analytics engineer path toward software.
• Shipped production dbt models with tests and docs.
• Maintained codebases with Git workflow, PRs, CI.
• Collaborated with product/engineering on data topics.
• Experience with data tooling, self-service analytics and governance.

Responsabilités

• Build backend features for the Research Data Platform using Python and FastAPI.
• Create and maintain dbt models with tests and docs.
• Collaborate with researchers, clinicians, and business teams to deliver value.
• Participate in code reviews and technical design discussions.
• Deliver BI solutions and dashboards; contribute to KPI governance via semantic layer.

Connaissances

Python

FastAPI

SQL

dbt

CI/CD

Code reviews

APIs

Outils

Git

Docker

Cube.js

LookML

Jupyter

Description du poste

The Company

Resilience Care is a leading medical remote monitoring player and a clinical research partner. Founded in France in 2021, our mission is simple: improve patient care.

Resilience Care is a leading medical remote monitoring player and a clinical research partner. Founded in France in 2021, our mission is simple: improve patient care.

Your role

In a nutshell: You join the Data & Analytics team to build and scale key technical foundations (backend + modern data stack) so data is reliable, usable and accessible for clinical, research and business teams.

Your impact: Increase delivery speed and trust in insights by improving automation, quality and documentation—and by ensuring metrics are consistently defined and reused.

Your day-to-day:

• Build backend features for our Research Data Platform (Python/FastAPI, PostgreSQL).
• Build & maintain dbt models (business logic, tests, docs, repo standards) to keep the data model robust for Self-Service analytics.
• Work closely with internal users (Research/Clinical/Business) to iterate fast and deliver value.
• Participate in code reviews, technical design, and continuous improvement of engineering standards.
• Support the team to deliver BI solutions such as dashboards and/or business analysis
• Contribute to metrics governance via a semantic layer (e.g., Cube.js) to align KPIs.
• Turn recurring manual work (reporting, quality triage, source onboarding) into reliable tooling, leveraging AI responsibly.

Your team

• Your future teammates: Anna - Analytics Engineer
• Your manager: Jean-Baptiste - Analytics Lead

“We'll work together closely: my role is to find technical solutions with the Product and Platform engineering teams to answer data-related business problems, and I stay a hands-on contributor — you won't be alone in the code. I value relationships built on honesty and trust, and I really enjoy working with curious people who are always eager to learn and to help. I'm based in Biarritz and have a lot of fun doing triathlon”

• Team’s extra: A small team of three, with direct exposure to product, research and clinical questions. Regular team time in Biarritz — seminars, team meetings, surfing, hiking and every possible Basque country cliché.

What we are looking for

• You are the right person if you can:
  • Ship production-grade Python (tests, code quality, reviews, CI).
  • Build and maintain backend APIs (FastAPI/Flask/Django or equivalent).
  • Data modeling with advanced SQL to deliver production dbt models.
  • Use AI tools as a multiplier while keeping strong engineering ownership.
  • Deploy and operate what you build (containers, environments, CI/CD).
  • Implement data tests/alerting and troubleshoot data quality issues.
• You are the right person if you have:
  • A structured, detail-oriented approach and a bias for automation.
  • Strong collaboration skills across technical and non-technical partners.
  • Pragmatism: prioritizing impact and maintainability.
  • Ownership, autonomy and accountability.
  • Curiosity and a continuous-learning mindset.
  • Clear communication and a solution-oriented attitude.
• You are the right person if you have already:
  • ~3–5 years across data and software (typical path: analytics engineer moving toward software).
  • Worked in a maintained codebase (Git workflow, PRs, CI).
  • Shipped production dbt models with documentation and tests.
  • Contributed to self-service data tooling and enablement.
  • Collaborated with product/engineering teams on data topics.
  • Operated in a context with high standards (security, compliance, sensitive data).
• It’s a plus if you:
  • Have experience with semantic layers / metrics governance (Cube.js, LookML, dbt Semantic Layer…).
  • Have exposure to healthcare data or other GDPR-heavy environments.
  • Have supported scientific users (Jupyter, RStudio, study data).

Why join us

• A mission that holds up: improving patient care with data.
• Rare scope at the intersection of backend and modern data engineering.
• A culture focused on automation and continuous improvement, with practical AI usage.
• Remote-friendly setup and an environment that values quality, standards and ownership.

Hiring process

• Screening - Talent Acquisition Specialist (15 min): validate motivation and role prerequisites
• Manager Fit - Jean-Baptiste Pajot (45 min) : position fit, and an honest conversation about the engineering-first, hybrid nature of the role
• Technical Case Study - prep (max 2h) + live discussion (1h30): you'll review a small realistic case mixing analytics engineering and software engineering
• Clinical Fit interview - Nicoleta (30 min): collaboration fit with Translational Research and Medical teams, since you'll help build tools for clinical and research users.
• Culture fit - Talent Acquisition Specialist (40 min) - assess alignment with our culture and remote ways of working
• Strategic Fit - Jullian Bellino (30 min) : collaboration fit, engineering standards

GDPR

Your personal data will be processed for the purposes of recruitment related activities, which include setting up and conducting interviews and tests for applicants, evaluating and assessing the results thereto, and as is otherwise needed in the recruitment and hiring processes. They will be available only for people involved in the process and erased after 2 years of inactivity.

Under GDPR and as Resilience attach great importance to privacy, please note that you have the right to request access to your personal data, to request that your personal data be rectified or erased. The Data Protection Officer can be contacted at [e-mail]

For more information, please check our privacy policy.