Azure Backend/Data Engineer (Python, FastAPI, Databricks)

Role Description

We are looking for an experienced developer with strong skills in Python and FastAPI, solid knowledge of Azure services, and hands-on experience with Databricks (PySpark). This role requires a versatile engineer who can work across backend development, cloud architecture, data integration, and optionally AI/ML deployment

Key Responsibilities

• Build and maintain APIs and microservices (FastAPI/Django/Flask) supporting data and AI workflows

• Design and implement scalable solutions on Azure (Apps, Containers, Storage, SQL)

• Work with Databricks (PySpark, Delta Lake, Delta Live Tables) to process and integrate data

• Implement Git-based workflows, testing, and CI/CD automation (GitHub Actions/Azure DevOps)

• Apply DevOps-first practices with automation and deployment using Databricks Asset Bundles (DAB)

• Ensure clean code, testing, and maintain high engineering standards

• Set up monitoring, logging, and alerting (Azure Monitor, Log Analytics, Cost Management)

• Contribute to solution architecture and propose improvements

• Collaborate with Data Scientists to deploy and maintain AI models in production (MLOps experience is a plus, not a must)

Qualifications & Competencies

• Solid knowledge of the Databricks ecosystem: architecture, Delta Lake, and Delta Live Tables

• Strong Python skills (OOP, testing, clean code) with experience in advanced data processing (preferably PySpark)

• Hands-on experience with API development and integration using FastAPI (or Flask/Django)

• Practical experience with Azure services: Apps, Containers, Storage, SQL

• Familiarity with DevOps practices: automation-first mindset, CI/CD pipelines, and deployment automation (DAB)

• Experience with Git, agile teams, and working in Scrum-based environments

• Knowledge of Docker, Terraform/ARM is a plus

• Strong problem-solving skills, ownership mindset, and ability to collaborate across teams

Nice to Have

• Basic understanding of Generative AI and Agentic AI use cases

• Experience in debugging and optimizing Spark jobs (Photon, Catalyst, query plans)

• Experience with ML model deployment, GenAI tools (LangChain, vector DBs, ML pipelines)