[Hiring] Principal Engineer – Data Platforms & MLOps @Codvo.ai

Role Description

We are looking for a hands-on Principal Engineer with deep expertise in Databricks to design, build, and scale enterprise-grade data platforms and MLOps pipelines. You will be the technical authority on how enterprises adopt and maximize Databricks — from ingestion to governance to machine learning deployment — and a mentor who raises the bar for engineering excellence.

• Platform Architecture: Design and implement end-to-end data architectures on Databricks Lakehouse, covering ingestion, transformation, storage, and analytics.
• Pipelines & Workflows: Build and optimize ETL/ELT pipelines with Delta Live Tables, Spark Structured Streaming, and workflow orchestration.
• Governance & Security: Implement Unity Catalog, fine-grained access controls, and compliance frameworks across enterprise data estates.
• MLOps at Scale: Operationalize ML models using MLflow, Model Registry, and CI/CD pipelines integrated with cloud DevOps tools.
• Performance & Cost Optimization: Tune Databricks clusters, jobs, and workflows for scale, speed, and efficiency across multi-cloud deployments.
• Client Advisory: Work closely with enterprise stakeholders to provide best practices, reference architectures, and accelerators tailored to their use cases.
• Mentorship & Standards: Guide engineers in Databricks best practices, enforce coding standards, and lead design/code reviews.

Qualifications

• 8+ years in large-scale data engineering / platform engineering, with 3+ years hands-on Databricks experience.
• Deep expertise in Databricks Lakehouse Platform (Delta Lake, Delta Live Tables, Databricks SQL).
• Governance & Security with Unity Catalog.
• MLOps with MLflow and model lifecycle management.
• Strong programming skills in PySpark, SQL, Python; experience with Scala a plus.
• Hands-on with cloud integration (AWS, Azure, or GCP) and DevOps pipelines (Terraform, GitHub Actions, Azure DevOps, etc.).
• Proven track record of building and scaling Databricks workloads in production for enterprise clients.