Description

You will own the data engineering, analytics, and MLOps lifecycle within cloud environments, primarily leveraging Databricks. Your work ensures reliable data pipelines, optimized cluster performance, and seamless model deployment.

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 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