Description

You will own the data engineering, analytics, and MLOps lifecycle within cloud environments, primarily leveraging Databricks.

This role is on-site.

Responsibilities

• Manage Databricks environments, including workspace configuration, cluster setup, performance tuning, and cost optimization.
• Build, execute, and monitor cloud data pipelines (Azure/AWS/GCP) to ensure scalability and data quality.
• Support MLOps workflows by deploying, monitoring, and retraining machine learning models using MLflow and version control best practices.
• Implement CI/CD practices via GitHub to automate testing, deployment, and environment management.
• Translate technical findings into executive-ready summaries and validate datasets for accuracy.

Required Skills

• 1–3 years of hands-on experience with the Databricks Platform.
• Proficiency in cloud data engineering on AWS, Azure, or GCP.
• Experience with MLOps tools, specifically MLflow, for model lifecycle management.
• Strong background in Data Engineering and Analytics Engineering.
• Competency in Git/GitHub for source control and CI/CD implementation.
• Ability to interpret complex data outputs and create clear diagnostic summaries.
• Bachelor’s or Master’s degree in Analytics, Statistics, Engineering, Mathematics, or related field.

Preferred Skills

• Experience optimizing production workloads across development, staging, and production environments.
• Proven ability to manage stakeholder requirements and deliver actionable business insights.

Key Skills

Education

Bachelor's or Master's degrees