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Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain scalable, reliable, and cost-efficient data pipeline architectures using Python, PySpark, Delta Lake, and MERGE INTO patterns.
Implement and drive automation in deployments, monitoring, incident remediation, and operational processes to enhance efficiency, quality, and reliability.
Manage infrastructure as code and automated CI/CD pipelines leveraging Terraform, Azure DevOps, GitHub Actions, Databricks tooling, and optimize compute resource usage and costs.
Minimum Requirements
Minimum 5 years of experience in data engineering, DataOps, DevOps, or a comparable technical role.
Advanced proficiency with Python, PySpark, SQL, Git, and Databricks ecosystem including Delta Lake, Unity Catalog, and Serverless SQL.
Experience with Infrastructure as Code tools such as Terraform and CI/CD tools like Databricks Asset Bundles, GitHub Actions, Azure DevOps, or GitLab.
Strong knowledge of Azure cloud services including security, networking, identity and access management, and storage integration; experience with AWS or GCP is a plus.
Ideal Candidate Profile
Experienced in operationalizing DataOps practices including data-contract enforcement, automated data-quality checks, and testing frameworks.
Comfortable managing end-to-end cloud-based data engineering environments emphasizing automation, governance, lineage, and security controls.
Skilled at cost optimization and resource management in large-scale Azure cloud and Databricks deployments.