Data Reliability Engineer

Resilience Care; France; Hybride; EUR 65 000 - 90 000; Plein temps

Résumé du poste

Resilience Care is seeking a Data Reliability Engineer to build, operate and secure the data platform that carries healthcare data to internal teams, clinical researchers and external partners.

You will own data pipelines end-to-end, implement IaC, monitor data quality with Grafana, and collaborate with SRE and product squads to scale dbt, PostgreSQL spaces and TypeScript integrations. This is a remote-friendly role within a mission-driven team.

Qualifications

• Ship production-grade Python with tests, typing, code quality, reviews and CI.
• Build and operate data pipelines and orchestration (Prefect, Airflow, Dagster or equivalent), including event streams (Kafka) and object/file transports (S3, SFTP).
• Write and optimize advanced SQL on PostgreSQL (indexing, query plans, incremental loads).
• Push dbt well beyond basic usage: macros and Jinja, multi-target projects, incremental strategies, tests, packages and CI integration.
• Deploy and operate what you build (Terraform, Helm/Kubernetes, containers, CI/CD), with meaningful monitoring and alerting (Grafana or equivalent).
• Read and contribute to TypeScript codebases with strong back-end API architecture knowledge (REST, versioning, authentication).

Responsabilités

• Build and operate data pipelines: ingestion from events/databases, transformation, export.
• Develop tooling for pipelines with SRE: IaC, deployment, secrets management, incident response.
• Watch over data quality and pipeline health with Grafana dashboards, alerts and observability.
• Own data contracts and data quality with automated tests (freshness, completeness, referential integrity).
• Deliver secure data exports to external partners and institutions.
• Provision and maintain PostgreSQL Research Spaces for clinical study teams.
• Scale dbt projects across targets and environments.
• Collaborate with requesters to scope needs and validate feasibility.
• Work with product squads on data emission and contribute to code (TypeScript) when needed.
• Participate in code reviews and ongoing engineering standards.

Connaissances

Python

Data pipelines

Terraform

Helm on Kubernetes

CI/CD

Grafana

dbt

TypeScript

PostgreSQL

SQL

Kafka

SRE collaboration

Outils

Prefect

Airflow

Dagster

S3

SFTP

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.
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.

Your role

In a nutshell: You join the Data Reliability Engineering (DRE) team to build, operate and secure the data platform that carries healthcare data from our production services to the people who use it — internal operational teams, clinical research users, and external partners receiving contractual data exports.

Your impact: Data arrives on time, complete, traceable and secure — and when it doesn't, we know before the users do. You turn one-off data requests and manual export procedures into automated, monitored, reproducible pipelines.

Your day-to-day:

• Build and operate data pipelines : ingestion from application events & databases, transformation, export.

• Build the tooling that handles those pipelines, hand in hand with SRE: infrastructure as code (Terraform, Helm on Kubernetes), deployment, CI/CD, secrets management, database operations and incident response.

• Watch over data quality and pipeline health: Grafana dashboards, alerting, and metric- and log-based observability, so failures are detected and diagnosed fast.

• Own data contracts and data quality: agree with the emitting squads on schemas, semantics and breaking-change rules, and enforce them with automated tests (freshness, completeness, referential integrity) that fail loudly before the data reaches its users.

• Deliver secure data exports to external partners and institutions.

• Provision and maintain dedicated PostgreSQL Research Spaces for clinical study teams, including access control and data scoping per study.

• Help the modelers scale their work: industrialize the dbt project so a single modelisation written by a Data Analyst is centralized, parallelized and applied across several targets.

• Work directly with the requesters: scope the need, challenge it, agree on what is feasible, deliver, and document.

• Work with the product squads on the data they emit: challenge their design upstream, and take part in the implementation on their side (TypeScript) when needed — you contribute to their code, they keep their roadmap.

• Participate in code reviews, technical design and continuous improvement of engineering standards.

Your team

• Your future teammates: Mélody Ballouard and Alric Gaurier — Data Reliability Engineers.

The team owns the data pipeline end to end: ingestion, transformation, exports, and the infrastructure underneath. You'll be exposed to all of it from day one.

• Your manager: Alric Gaurier

• Team's extra: A small team with direct exposure to clinical research, medical and operational stakeholders. What you build is used by named people you talk to every week — not by an anonymous backlog.

What we are looking for

• You are the right person if you can:

  • Ship production-grade Python (tests, typing, code quality, reviews, CI), and use AI tools as a multiplier without letting go of engineering ownership.

  • Build and operate data pipelines and orchestration (Prefect, Airflow, Dagster or equivalent), including event streams (Kafka) and object/file transports (S3, SFTP).

  • Write and optimize advanced SQL on PostgreSQL (indexing, query plans, incremental loads).

  • Push dbt well beyond basic usage: macros and Jinja, multi-target projects, incremental strategies, tests, packages and CI integration.

  • Deploy and operate what you build (Terraform, Helm/Kubernetes, containers, CI/CD), with meaningful monitoring and alerting (Grafana or equivalent) — and lead the diagnosis when something breaks.

  • Read and contribute to TypeScript codebases when the pipeline meets the application side, with a solid grasp of back-end API architecture (REST design, versioning, authentication, pagination, contracts).

• You are the right person if you have:

  • Strong communication skills — this is not a nice-to-have in this role. You will spend real time with non-technical requesters.

  • The ability to translate an operational or scientific need into a technical specification, and to say no (and explain why) when a request is not feasible or not compliant — with the support of the Legal/Compliance department.

  • Comfort collaborating with SRE on shared infrastructure, and with scientific profiles on clinical trial data.

  • Rigor with sensitive data: you treat patient data with the caution it deserves, by reflex.

  • Ownership, autonomy and accountability, including on-call-style responsibility for what you ship — with a structured, detail-oriented approach, a bias for automation, and a focus on impact and maintainability.

  • Curiosity and a continuous-learning mindset — the reflex to go beyond your own stack: reading a product squad's code, digging into an API or an infrastructure layer you don't own, and learning what you need to unblock yourself instead of waiting for someone else.

• You are the right person if you have already:

  • ~4–6 years in data engineering, platform engineering, or backend with a strong infrastructure component.

  • Operated production pipelines you were accountable for (not just built them).

  • Written and reviewed infrastructure as code in a team setting.

  • Delivered data to stakeholders outside your own team, and handled the conversation that goes with it.

  • Worked in a maintained codebase (Git workflow, PRs, CI).

  • Operated in a context with high standards (security, compliance, sensitive data).

• It's a plus if you:

  • Have exposure to healthcare data, GDPR-heavy or HDS-hosted environments.

  • Have supported scientific or clinical research users (study datasets, cohort extraction, data requests from investigators).

  • Have handled contractual data exchanges with external partners (data transfer agreements, delivery SLAs, encryption requirements).

Why join us?

• A mission that holds up: improving patient care with data.

• Real ownership over a full platform — pipelines and the infrastructure they run on — not a narrow slice.

• Direct contact with the people your work serves, including clinical research teams running active studies.

• A culture focused on automation, observability and continuous improvement, with practical AI usage.

• Remote-friendly setup and an environment that values quality, standards and ownership.

Hiring process

1. Screening - Elena (15 min): validate motivation and role prerequisites

2. Manager Fit - Alric (30 min): position fit, and an honest conversation about the ops-and-build nature of the role

3. Technical Case Study - live session (1h30), no preparation: you work through a small realistic case with us — pipeline design, infrastructure and data export constraints. We look at how you reason and what you ask, not at a polished deliverable.

4. Team fit : Fit interview Mellody, Anna, Guillaume (30 min): collaboration fit with Research/Clinical and operational requesters

5. Culture fit - Elena (40 min): assess alignment with our culture and remote ways of working

6. Strategic Fit Jullian (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 ----- For more information, please check our privacy policy.

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