Senior Cloud Engineer – Databricks, Python

Your Responsibilities

Hands-On Platform Development & Scouting: Build, automate, and evolve the Databricks and Azure environment using Infrastructure as Code. You will actively scout and test new technologies, track the Databricks roadmap, and resolve technical debt to keep the platform modern and reliable.

Platform Adoption & Team Alignment: Help engineering teams at both Datamole and the client get the most out of the platform. You will identify what individual teams need, turn those findings into practical guidelines and platform improvements, and connect people across teams to drive adoption forward.

Cloud Applications & Data Pipelines: As a secondary part of the role, design and build cloud applications and data pipelines in Python that optimize industrial processes and run ML workloads at scale, collaborating with our data scientists and device-side (Rust) engineers.

Modern AI Workflows & Mentorship: Actively integrate agentic AI coding tools into our daily workflows, continuously refining our engineering practices while retaining strict ownership of technical decisions and software quality. Contribute to code reviews and share knowledge across the team.

What We’re Looking For

5+ years of professional cloud, data, or software engineering experience, including delivering, maintaining, and scaling production systems in a cloud environment. Though we care much more about your engineering foundation and how quickly you pick up new concepts than the exact number of years on your CV.

Cloud & Data Platform Skills: Hands-on experience with Databricks (e.g., Unity Catalog, Delta Lake, PySpark, Asset Bundles) and Microsoft Azure (Storage, Key Vaults, networking, Azure SDK) - or strong experience with comparable cloud platforms and the drive to go deep into the Databricks ecosystem.

Infrastructure & Code: Practical experience with Infrastructure as Code (e.g., Terraform), CI/CD pipelines (e.g., GitHub Actions), and solid proficiency in Python 3 and SQL.

Engineering Foundation: A strong grasp of software engineering principles, automated testing, and the ability to independently solve complex engineering problems.

Curiosity Beyond the Cloud: An interest in how things work closer to the device side (Linux, edge computing), with a willingness to learn Rust in the future and potentially contribute to our device-side applications over time.

Communication & Culture: A collaborative mindset with a focus on open communication, trust, and shared responsibility. At Datamole, we deeply value fairness, long-term thinking, and a healthy work-life balance (no crunch culture). Professional working proficiency in English.

Nice to Have

Experience with modern Python web frameworks (e.g., FastAPI) and data libraries (e.g., Polars).

General familiarity with Machine Learning and AI concepts (you don't need to be a data scientist, but it helps to understand their workflows when supporting ML projects on the platform).

Familiarity with monitoring and observability tools (OpenTelemetry, Prometheus, Grafana).