Senior / Staff Full-Stack Data Engineer – Databricks (Pune)

Codvo.ai; Pune District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

A fast-growing tech company is seeking a Senior / Staff Full-Stack Data Engineer with deep expertise in Databricks. The role involves designing and maintaining scalable data pipelines while collaborating with data scientists to productionize machine learning models. Ideal candidates should have strong experience with Apache Spark, data governance, and CI/CD workflows. This position promises to enhance data quality and optimize costs through innovative solutions.

Qualifications

• Strong hands-on experience with Databricks, Apache Spark, and Delta Lake.
• Proven experience building and operating production-grade data pipelines.
• Experience operationalizing machine learning models and inference pipelines.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines on Databricks using Spark and Delta Lake.
• Collaborate with data scientists to productionize models and optimize performance.
• Implement CI/CD, DevOps, and MLOps best practices for data pipelines.

Skills

Databricks

Apache Spark

Delta Lake

Data governance

CI/CD workflows

DevOps

MLOps

AWS

Azure

Job description

Job Title: Senior / Staff Full-Stack Data Engineer – 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.

Job Description: Senior / Staff Full-Stack Data Engineer – Databricks

Role Overview

We are seeking a Senior / Staff Full-Stack Data Engineer with deep Databricks expertise to design, build, and operate scalable data and machine learning pipelines. This role works closely with data scientists, platform teams, and application engineers to productionize analytics and ML workloads with high reliability, performance, and cost efficiency.

Key Responsibilities

• Design, build, and maintain ETL/ELT pipelines on Databricks using Spark, Delta Lake, and Databricks Workflows
• Build and operate batch and real-time data pipelines for ingestion, transformation, and orchestration
• Operationalize machine learning inference pipelines authored by data scientists (batch and real-time)
• Ensure consistency between model training and inference environments
• Implement data quality checks, validation rules, monitoring, alerting, and automated recovery
• Collaborate with data scientists to productionize models and optimize inference performance and cost
• Implement CI/CD, DevOps, and MLOps best practices for data pipelines and ML workflows
• Optimize compute, storage, and job configurations for performance and cost efficiency
• Implement and manage enterprise data governance using Unity Catalog (schemas, lineage, ownership, documentation)
• Work with Databricks infrastructure and platform configurations

Required Skills & Experience

• Strong hands‑on experience with Databricks, Apache Spark, and Delta Lake
• Proven experience building and operating production‑grade data pipelines
• Experience operationalizing machine learning models and inference pipelines
• Strong understanding of data reliability, observability, and monitoring practices
• Experience with CI/CD, DevOps, and MLOps workflows
• Experience working with cloud platforms (AWS or Azure)
• Familiarity with Unity Catalog and enterprise data governance concepts
• Experience with spec‑driven development and coding agents

Nice to Have

• Experience with Databricks infrastructure tuning and cost optimization
• Exposure to streaming frameworks and real‑time data processing
• Experience with Infrastructure‑as‑Code (Terraform or similar)

What Success Looks Like

• Reliable, scalable, and cost‑efficient Databricks data and ML pipelines
• Smooth productionization of ML models with strong collaboration across teams
• High data quality, observability, and platform stability
• Well‑governed data assets with clear ownership and lineage