Senior Databricks Engineer

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: Design, build, and maintain Databricks data pipelines (ETL/ELT) for ingestion, transformation, and orchestration using Spark/Delta Lake/Databricks Workflows.

Responsibilities

Design, build, and maintain Databricks data pipelines (ETL/ELT) for ingestion, transformation, and orchestration using Spark/Delta Lake/Databricks Workflows; Operationalize machine learning models by building inference pipelines that invoke models authored by data scientists (batch or real-time), ensuring consistency between training and inference environments; Ensure data reliability, quality, and observability through robust validation, monitoring, alerting, and automated recovery mechanisms; Collaborate closely with data scientists to productionize models, manage model deployment lifecycles, and optimize inference performance and cost; Implement best-practice DevOps/MLOps processes such as CI/CD for pipelines, model versioning, environment promotion, and infrastructure-as-code; Optimize performance and cost across compute clusters, jobs, and storage layers; Implement and manage the enterprise data catalog, including schema design, table ownership, lineage, governance, and documentation using Unity Catalog

Requirements

Level: Expert (8+ years); Databricks platform experience; Python development for data processing and ETL pipelines; Unity Catalog knowledge; AWS data services (S3, IAM, VPC, potentially Glue/Lambda); Data lake/lakehouse architecture patterns; Dashboard building experience; Experience with coding agents and best practices (spec-driven development, etc.)

Nice to have

RESTful API design and development (Flask, FastAPI, or similar); Authentication/authorization patterns (OAuth, API keys, IAM roles); Query optimization and performance tuning; PySpark optimization experience; ML/AI pipeline experience; Databricks AI/BI