Job description
Role overview:
We’re looking for a mid–senior Data Engineer (4–6 years of experience) who can take ownership across data architecture, ML/GenAI pipeline PoC-to-Production, and backend integrations.
You’ll play a key role in turning experimental models into reliable product features, shaping the data platform, and raising engineering quality across the team.
You’ll report to the Engineering Manager and collaborate closely with our Data Science team, supporting them in taking research-grade pipelines into a production-ready environment.
This is a great fit for someone who enjoys both building and improving systems end-to-end: data, backend, infrastructure, and everything in between.
In 2024, we raised €5.5 million from Elaia and Polytechnique Ventures to accelerate our mission.
As a Database Engineer, you will work on:
- ML & GenAI Production
- Convert experimental DS/ML/GenAI workflows into scalable, reliable production pipelines
- Integrate OpenAI, AWS Bedrock, and internal models into product-critical processes
- Work with Data Scientists to design model-serving strategies and orchestration flows
- Deploy and monitor multi-stage workflows leveraging AWS (Lambda, Batch, Glue, ECS)
- Backend & API Integration
- Build and maintain data-driven features using FastAPI and Django
- Partner with product teams to expose new data capabilities through clean, well-structured APIs
- Help define and enforce backend/data patterns, code style, and technical quality standards
- Data Pipelines & Platform
- Improve and extend ingestion pipelines using PySpark, Delta Lake, and AWS
- Work with structured, semi-structured, and unstructured datasets at scale
- Maintain Delta tables, audit systems, and metadata layers
- Support the evolution of our data models, lakehouse architecture, and storage strategies
- Engineering Quality & Ownership
- Participate in code reviews, drive best practices, and improve the engineering culture
- Contribute to architectural discussions and technical decision-making
- Help shape internal standards for data validation, reliability, and observability (logs, metrics)
- Tech Stack You’ll Use
You don’t need to know everything on day one — but experience with most of these is expected:
- Python, SQL
- FastAPI, Django
- PySpark, Delta Lake
- PostgreSQL, DynamoDB
- AWS: Lambda, Batch, Glue, EC2, ECS, S3
- OpenAI / AWS Bedrock APIs
- Haystack (search/LLM pipelines)
- Docker, GitHub Actions
- Bonus:
event-driven architectures, Terraform, CI/CD, observability tooling
Preferred experience
- MANDATORY: 4–6 years of experience as a Data Engineer or Backend/Data hybrid
- Strong Python & SQL skills, with experience building production-grade pipelines
- Hands-on experience with large-scale data processing (Spark) and lakehouse technologies (Delta Lake)
- Proven ability to productionise ML/GenAI pipelines and integrate them into backend systems
- Familiarity with cloud infrastructure (AWS preferred)
- Good communication skills and a collaborative mindset
- Ability to take ownership, make sound technical decisions, and maintain high engineering standards
Recruitment process
- 30 min chat with HR team for a culture-fit introduction call
- Case study
- 30 min chat with the hiring manager (Harsha, Engineering Manager)
- Office tour & team meeting (if Paris-based)
D&M celebrates diversity and inclusion.
If you’re excited about this role but don’t meet every single requirement, we still encourage you to apply.
D&M believes diversity drives innovation and is committed to creating an inclusive environment for all employees. We welcome candidates of all backgrounds, genders, and abilities to apply. Even if you don’t meet every requirement, if you’re excited about the role, we encourage you to go for it—you could be exactly who we need to help us create something amazing together!
