Senior Data Engineer (Product-focused) (m/w/d)

Stellenbeschreibung:

Thryve is a pioneering B2B SaaS company operating at the forefront of health tech infrastructure. We specialize in aggregating billions of patient-generated data points daily, analyzing them in a real-time system to provide cutting‑edge insights to our clients. Our clientele includes major health insurance providers across Europe and providers of medical‑grade digital therapies.

Founded by three visionaries and supported by venture capital firms specialized in biotech and B2B software, Thryve is driven by a robust, no‑nonsense performance culture. We are committed to winning in the health tech space and support our team members by accommodating the life rhythms necessary for maintaining health and high efficiency. We measure outcomes and expect a fervent drive for performance and value at every level of the company.

Benefits and Work Environment

Equity Participation: We offer virtual shares in the company, ensuring that when Thryve grows, so do you.
Flexible Work Locations: Our office in Berlin Kreuzberg is a hub for meeting and retreats, fostering productivity in a collaborative environment. You will have the flexibility to balance remote and office work to optimize performance.
Comprehensive Benefits Package: At the start of employment, team members can choose one of the following:
- BVG Job Ticket: Thryve covers the cost of the Deutschland job ticket
- Urban Sports Club Membership: Enjoy a subsidised membership
- Swapfiets Bike Subscription: Thryve will subsidise a bike from Swapfiets

At Thryve, we believe that a supportive environment and smart benefits lead to empowered employees. We're not just creating solutions; we're enhancing lifestyles, promoting well‑being, and sharing our success with every team member. Join us, and help shape the future of health tech.

We build infrastructure that turns messy, real‑world wearable data into reliable, actionable insights. Unlike traditional systems, our data reflects human behaviour—consistent, noisy, and full of edge cases.

We're looking for a (Senior) Data Engineer who is motivated by exactly that challenge.

This role is for someone who enjoys going deep: understanding biological signals, questioning assumptions in data, and building systems that handle real‑world complexity with precision.

What You'll Do

You’ll be part of our AI and Data Analytics team, working at the core of our data platform.

Design, build, and operate microservices for real‑time data processing
Own and evolve our data lake (ClickHouse) and its ingestion pipelines
Develop and maintain robust streaming and CDC pipelines (Kafka, Debezium)
Improve data quality and reliability in the face of noisy, inconsistent wearable data
Collaborate closely with analytics and AI teams to enable high‑quality downstream use cases
Act as a technical sparring partner for data modelling and pipeline design decisions

What Makes This Role Exciting

Wearable data is fundamentally unreliable:

Devices fail or behave inconsistently
Users forget, misuse, or change behaviour
Biological signals vary widely between individuals

You’ll build systems that don’t just process data—they interpret and stabilise it. Curiosity about how wearables work and how data is generated (and not just how it’s processed and stored) is therefore a key requirement.

Must‑Haves

Strong analytical thinking and a desire to deeply understand data problems
Solid experience with SQL and data modelling
Hands‑on experience with ClickHouse (or comparable analytical databases)
Experience building and operating data pipelines (Kafka, CDC, streaming)
Experience developing microservices in Python for real‑time processing
Experience deploying applications in Kubernetes using ArgoCD and configuring via Helm charts
Ability to work independently and take ownership of systems end‑to‑end (we have a strong 'you build it, you own it' culture)

Nice‑to‑Haves

Experience working with wearable, health, or time‑series data
Interest in biology, physiology, or medical applications
Experience with statistical analysis or data validation techniques
A strong sense of data quality and curiosity‑driven exploration
Experience with unstructured data pipelines (NoSQL, S3) and health data standards (HL7/FHIR)
Familiarity with feature store architectures