Data Analytics Engineer

About us

Implicity is a digital MedTech, that brings outstanding innovations to cardiologists, thanks to Big Data and Artificial Intelligence.

Thanks to our leading cardiac remote monitoring platform, it’s way easier to manage data and predict patient issues, so that cardiologists can bring the best care at the best time.

To put it simply, when you join Implicity, you’ll contribute to save lives with us

Dr Arnaud Rosier (cardiologist and AI researcher) & David Perlmutter (engineer and entrepreneur), co-founded Implicity in 2016.

• 10+ years later, a French Start-Up / Scale-Up is a real game changer in the healthcare market, literally shaping the future of cardiology.

• 250+ hospitals / medical centers are already using our solutions, covering 100 000+ patients.

At Implicity, you will find the greatest experts in data science, engineering, clinical, regulatory, IT, sales, customers success, etc. working together.

This amazing team already managed to make Implicity a clear European leader, and we will very soon do the same in the US market.

In a nutshell, thanks to Implicity:

Patients get a far better care

Doctors’ life is far easier, they can have a far better focus on prevention/treatment, and not admin/data burden

Healthcare payers (Social Security in France) eventually pays a far lower price (preventing/monitoring instead of treating/hospitalizing)

It can start as soon as you can!

Job and recruitment context

As an Analytics Engineer in the Data Team, you will contribute to the design and evolution of analytical pipelines that powers our business stakeholders insights. Your goal in this job is to consolidate our single source of truth, maintaining analytical models, and a data stack that scales with Implicity's growth across Europe.

Reporting Structure

Direct Report:  Damien (Data Engineer Manager)

Collaboration:  You will be part of the Analytics section of the Data Platform team, collaborating within a tech team of 8 people.

Your Profile and Mindset

Experience Profile

• Experience : 3+ years as an Analytics Engineer or similar data & analytics role

• Familiarity with Modern Data Stack : data modeling, ETL/ELT, and orchestration tools.

• Strong interest in the healthcare sector and the challenges of medical data normalization (HL7, FHIR).

• Strong ownership mindset and attention to data quality.

• Fluent in English and French.

Your missions

• Transformation Pipelines (ETL): Develop and maintain scalable data pipelines ( dbt, Dagster ), comprehensive semantic layer and robust transformation pipelines to convert raw data into analytics-ready datasets.

• Semantic Layer: Build and maintain business oriented models using cube.dev ensuring BI-ready datasets to access to critical performance indicators.

• Data Quality & Traceability: Implement automated testing and monitoring to ensure the highest levels of documentation, data quality and availability from S3 to Metabase

• Stakeholder Partnership: Act as a technical partner to Business Unit teams, translating business questions into technical requirements and analytical frameworks.

• Run & Support: Investigate and resolve data quality incidents to ensure the reliability of our analytics platform.

• Future of Analytics at Implicity: Enable self-service analytics and emerging agentic solutions empowering users to independently discover insights and optimize workflows. ( Metabase )

To succeed in your mission, you will be part of a skilled and collaborative team working with modern data technologies. We encourage continuous learning and innovation, with dedicated time for experimentation and skill development.

At Implicity, you will have a weekly meeting with your manager, to help you succeed in your mission, and continuously improve your skills.

Each team works with quarterly OKR, to be crystal clear, fair and honest with your targets.

The annual appraisal is a shared exchange moment, focused on your development.

Our Analytics Technical Stack

The following is our current stack, we don’t expect you to be an expert in every single tool.

• Lakehouse : AWS S3, Parquet, Apache Iceberg

• Query Engine : AWS Athena

• Transformation & Orchestration : dbt, Dagster

• Governance & Lineage : DataHub

• Semantic & BI : Cube.dev, Metabase

• Infrastructure : AWS, Kubernetes (EKS), Terraform, Docker, GitLab CI/CD

Hard skills and Soft skills

Technical Expertise:

• SQL Mastery: Proficiency in SQL, DBT to transform raw data into high-quality analytical assets. Comfortable with window functions, indexing strategies, and knowledge to optimize complex queries.

• Modern Data Stack: Experience Python orchestrators (Airflow, Dagster …) building and maintaining data pipelines

• Governance Advocate: Strong commitment to data governance, lineage, and documentation.

• BI : Data visualization expertise with tools like Metabase, Superset, Tableau, or similar.

• Engineering Mindset: Version control (Git), CI‑CD, documentation, and best practices are daily basis tools.

Professional Skills:

• Collaborative mindset: Strong communication skills to work with technical and non-technical stakeholders.

• Team player: Genuine passion for teamwork and dedicated to elevating your colleagues towards collective success.

• Self-driven & autonomous: Comfortable working independently while contributing to cross-functional teams.

• Curiosity & adaptability: Eager to learn and develop expertise in emerging technologies.

• Problem-solving orientation: Proactive approach with strong analytical and prioritization skills.

• Quality-focused: Committed to code quality, testing, and documentation.

• AI-Assisted: Ability to leverage tools like Cursor or Claude to accelerate delivery while maintaining high code quality.

A Note on Applying: We know the perfect candidate doesn't exist. If you believe you possess the core required experience and strongly align with this mindset, we highly encourage you to apply.

Recruitment process

1. 1st HR Contact with Astrid (Talent Acquisition Manager) – 45 min - G-meet

2. Job / Manager Interview  with Damien (Data Engineer Manager) – 45 min - G-meet

3. Technical interview with Louise & Stefan (Data team members) (90 min) - On-site

4. Fit Interview with Louay (CTO) - 1 hour - On-site or Remote

5. Reference Check & Offer (usually follows within 72 hours )

Depending on your availability, the recruitment process should last less than 3-4 weeks.

General information

Salary

• For this job (full-time), you have a base salary between €52k-57k depending on your experience

• Eligible for stock option (BSPCEs) according to the company's existing rules

Benefits

• Health care plan: Alan (50% employer)

• Luncheon voucher: 9€ (50% employer)

• Transport: 50% of your pass OR sustainable mobility pass

Remote work & Location

• 3 days per week (progressively)

• Location: 29 rue du Louvre, 75002, PARIS