Principal Engineer – Data Platforms & MLOps (Databricks)

Company Overview 
At Codvo, software and people transformations go hand-in-hand. We are a global empathy-led technology services company. Product innovation and mature software engineering are part of our core DNA. Respect, Fairness, Growth, Agility, and Inclusiveness are the core values that we aspire to live by each day.

We continue to expand our digital strategy, design, architecture, and product management capabilities to offer expertise, outside-the-box thinking, and measurable results.

Role Overview

Key Responsibilities

• 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.