ENLYZE connects machines in factories to the cloud and turns their raw signals into data that people and AI agents can work with. As one of four backend engineers on our platform, you own the cloud side of that: the streaming services that store machine data in our cloud data warehouse, the API and MCP server that customers and their AI agents build on, and the agentic workflows that help set up the platform itself, for example by mapping thousands of cryptic PLC tags to meaningful process values.
About ENLYZE
Manufacturers sit on a wealth of machine data they can't use. Getting value out of it means solving two hard problems:
Access – getting to the data at all. Industrial data is locked in PLCs, sensors and decades-old control systems that speak heterogeneous, often undocumented protocols. Our connectors and edge devices break these sources open.
Structure and context – making the data mean something. A raw PLC tag like
DB12.DBD40means nothing to a person or to an AI model. It has to be mapped to a real process value and enriched with operational context: machine state, the production run or batch, the product being made and the environmental conditions.
The ENLYZE Manufacturing Data Platform solves both. Production teams use it to reduce downtime, trace scrap back to its root cause, improve their process parameters and drive data-driven continuous improvement.
It is also the foundation industrial AI needs. AI doesn't work without real-world, contextualized data, and most factories still can't access or use theirs, even as AI adoption accelerates. We close that gap, from the machine on the shop floor to the AI agent that reasons about it.
The team and how we work
We are a small product team that works as one:
two engineers on the edge (our edge device runtime, connectors and industrial protocols)
two engineers on the platform (you'd be one of them)
two frontend engineers
one product manager
Depending on what matters most, we work on separate streams or all on the same one. Either way, we build every feature vertically: a new connector isn't done when it reads data on the edge. It's done when it's integrated into the platform and configurable through the API, the MCP server and the UI. That way we solve the end user's actual problem, not a slice of it. Owning the whole path from the machine to the user is one of the things that sets ENLYZE apart from other providers, and it means you'll regularly work across the edge/cloud and backend/frontend boundaries.
Our tech stack
Cloud: Python services (FastAPI and Django) on PostgreSQL/TimescaleDB, RabbitMQ for streaming, running on Kubernetes in Google Cloud, monitored with Prometheus and Grafana.
Edge: Rust. Our edge agent and protocol libraries (e.g. for Siemens S7) run on our own SPARK devices in customer factories.
Where we're heading: we're considering moving cloud services to Rust as well. If you know Rust or want to grow into it, you'll help shape that decision.
What you'll be doing
Own backend services end to end: design, build, ship and run them, from an idea to a feature, from an alert to a deployed fix.
Make our streaming pipelines robust for real-time machine data: no lost data, predictable latency, clean recovery from failures.
Grow our API and MCP server so customers, partners and their AI agents can do everything with the platform that our own UI can.
Build the infrastructure for agentic workflows, such as agents that map PLC tags to process values or configure new machines.
Ship features vertically with the team: work with the edge and frontend engineers and our product manager so every feature reaches the user complete.
Keep the platform reliable: CI/CD, observability, and the occasional piece of legacy code that deserves a better life.
Who we are looking for
Must haves:
5+ years as a backend engineer, building and running production services in Python (we use FastAPI and Django on PostgreSQL).
You've operated what you built: CI/CD, Kubernetes, monitoring and alerting.
Experience with streaming or message-driven systems (we run RabbitMQ; Kafka, Flink or similar count just as much).
Security as a default: authentication and authorization, multi-tenant data isolation, secrets handling.
You use AI coding tools every day and have a grounded view of where they help and where they don't.
Bonus points if you have:
Experience with Rust, or a strong interest in learning it.
Experience with IoT fleets and timeseries data at scale (e.g. TimescaleDB, Prometheus/Grafana).
Experience with data modelling using ontologies or knowledge graphs (extra points for the manufacturing space, e.g. ISA-95).
Experience in working in small fast-moving teams
German (not required: the team works in English).
Our values
Ownership, agency and continuous improvement. You see things through, and you leave code, processes and docs a little better than you found them.
Ideas over job titles. Anyone can question anything, including the founders.
Clear communication. You explain your decisions and reasoning, in writing and in conversation, so the next person doesn't have to rediscover them.
Open, direct feedback. Given respectfully and taken seriously, in both directions.
Why we might not be a good fit
ENLYZE is an exciting place to work, but it's not for everyone. We might not be a great fit for you if:
You're seeking a leadership role: we're a small, hands-on team, and this role focuses on individual contribution rather than management.
You're uncomfortable with legacy code: our product has been running in production for years, so you'll work with older and less well-maintained code as well.
You prefer working in large, established teams: if you're looking for the structure of a big company, ENLYZE may not be the right fit. We're still refining our product-market fit and operate with a startup mentality.
You're expecting hyper-growth: we've grown significantly over the past 18 months, but we're aiming for steady, sustainable growth rather than explosive growth.
Salary
€90,000 – €110,000 gross per year, plus ESOP, so you share in the company's growth. Where you land depends on your experience and on how much of the role you can cover from day one.
Benefits
30 days of vacation plus public holidays
Urban Sports Club (M membership for €10/month) and JobRad bike leasing
Your choice of hardware and OS
Our hiring process
Get to know Interview (30 Minutes):
This interview is designed to get the overall picture on what you as the candidate are looking for, what experience and skills you bring, and to explain who we are. The interview is performed by one of your future team members.
This allows you and us to assess whether you are a great addition to the team, and to dig into any areas of potential misalignment based on the application. We are looking for proactivity and agency, directness, good communication skills, an awareness of the impact of your work, and evidence of iteration / a growth mindset.
Candidates who are unsuccessful at this stage receive a short email with feedback very soon after the call.Technical Interview (60 Minutes):
In this round, you will meet one or two future team members. This round is usually 45-60 minutes and will focus on a mix of experience and technical skills. It will be a free form conversation where you will be challenged on different technical topics.
Founder Call (30 Minutes)
This is a call with one of the founders and your chance to ask more general questions about ENLYZE, the business and our overall strategy. In this session we will further probe on your motivation and cultural fit.
Test Workday
The final stage of our interview process is a paid full day of work with us, which we can flexibly arrange around your schedule. We are not able to bypass this stage. So if you are not interested in this final round, unfortunately we will have to part ways and can not consider you for the role.
How to apply
Just send us your CV and any further information you deem relevant to hiring@enlyze.com use the job title in the subject line.