Full-Stack Data Engineer – Data & ML Automation (Databricks) (Pune)

Job Title: Full-Stack Data Engineer – Data & ML Automation (Databricks) 

About Us

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology.

Overview

We are seeking a Fullstack Data Engineer who can operate at the intersection of data engineering , fullstack development , and testing/automation . This role will focus on building internal automation tooling, validating data and ML workflows, and developing lightweight web interfaces or dashboards that help teams interact with data and model outputs more effectively.

The ideal candidate is a versatile engineer who can seamlessly switch between CLI tooling , pipeline testing , backend glue code , and rapid visualization apps .

Key Responsibilities

Automation & Tooling

• Develop CLI tools, scripts, and utilities to automate repetitive workflows across the data platform, including:
  • Pipeline execution
  • Governance and metadata synchronization
  • Environment setup
  • Test harness creation
• Automate Databricks workflows , job deployments, environment provisioning, and MLOps operations using:
  • Databricks APIs
  • Terraform
  • Databricks SDK

Data & ML Workflow Testing

• Design and implement:
  • Integration tests
  • End‑to‑end pipeline tests
  • Validation frameworks for Databricks ETL/ELT pipelines and ML inference workflows
• Improve reliability, observability, and overall engineering productivity across the data & ML team.

Internal Tools & Dashboards

• Build quick internal applications using React , Streamlit , or similar frameworks to:
  • Visualize data flows
  • Provide model inference demos
  • Enable operational or configuration controls
  • Develop internal productivity and monitoring dashboards

Engineering Practices

• Apply best practices around:
  • Spec‑driven development
  • Coding agents and automation patterns
  • CI/CD workflows for data/ML systems

Nice-to-Have Skills

• Experience creating dashboards and visualizations using Databricks AI/BI or any modern BI tool (Power BI, Tableau, Looker, etc.)