Back End Developer

Role: Backend Engineer for Analytics Workspaces Platform

Duration: 4+ months

Location: Remote (Romania)

Mode of Interview: Video

Must have:

• Azure cloud engineering + API design

Must have:

• Strong software engineering background: API design, modular monolith architecture, testing, CI/CD, and containerization
• Proven experience building and maintaining production APIs and backend automation at scale
• Deep cloud architecture experience on Microsoft Azure (compute, networking, identity, managed services, infrastructure-as-code)
• Proficiency in Python (our primary automation language)
• Experience designing systems with clear bounded contexts, contract-driven APIs, and evolvable module boundaries

Preferred:

• Experience building developer platforms, internal tools, or self-service portals
• Experience working with infrastructure-as-code such as Terraform
• Familiarity with Databricks, Power BI, or comparable analytics platforms — not as a user, but understanding their API surfaces and operational models
• Experience with FinOps practices — cost allocation, chargeback models, budget enforcement
• Knowledge of event-driven architectures, workflow orchestration, or job scheduling at scale
• Experience working in a platform engineering or platform product team

Mission:

Accelerate the analytics engineering lifecycle and maximize engineers' deployment velocity, operational visibility, and financial control.

We are a team own the analytics workspace components of the data platform: Databricks and Power BI. We provide the tooling, automation, and guardrails that let analytics engineers move fast without breaking things or blowing the budget. We build CI/CD pipelines for notebooks and reports, cost monitoring dashboards, workspace provisioning automation, and self-service templates that make the right thing the easy thing.

We don't just administer workspaces. We treat them as a platform product, and we need strong backend engineering to make that real.

What you'll do

• Design, build, and evolve the analytics workspace platform APIs: the programmatic interface through which analytics engineers provision, configure, discover, and govern their workspaces, clusters, and reports
• Architect and implement a modular monolith backend that orchestrates across Databricks APIs, Power BI REST APIs, and Azure infrastructure — resilient, observable, and easy to extend
• Build and maintain CI/CD pipelines for analytics artifacts — notebooks, dashboards, semantic models — so that promoting from dev to production is automated, testable, and auditable
• Design and implement automation workflows: cost attribution per team/project, budget alerts, auto-scaling policies, idle resource detection, and compliance guardrails
• Instrument and surface operational visibility: deployment metrics, run history trends, failure rates, and SLA adherence across all analytics workloads
• Own the workspace governance model as code: access control, environment separation, secret management, and data source connection policies enforced through automation, not manual steps
• Build self-service tooling and templates that reduce time-to-first-query and time-to-first dashboard for analytics engineers