Analytics Software Engineer

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., She recently joined the team and is already growing fast on our stack and data model. She’ll be your closest day-to-day teammate, and part of the deal is that the three of us level each other up.
• 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., + 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.

• 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).

• 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 Manager (40 min) - assess alignment with our culture and remote ways of working
• Strategic Fit - Jullian Bellino (30 min) : collaboration fit, engineering standards

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. We build remote monitoring solutions in oncology, gastroenterology and psychiatry, powered by ePRO collection and AI techniques. Our platform helps care teams detect side effects early for continuous and proactive care, while our patient app helps people track and manage symptoms with tailored resources. Our solutions optimize care pathways, enrich continuous patient understanding, and accelerate clinical research through the collection, structuring and detailed analysis of real-world data. Today, our solutions are deployed in routine care for 35,000 patients across 200+ healthcare institutions, and also support around twenty academic and industry clinical studies. We put data at the service of care and therapeutic innovation, with the ambition to enable every patient to benefit from personalized medicine.