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

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, andDatabricks Workflows Build and operate batch and real-time data pipelines for ingestion, transformation, andorchestration Operationalize machine learning inference pipelines authored by data scientists (batch andreal-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-productive 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 .