👨‍💻 About the role

We’re looking for a Data Operations Engineer to keep our data platforms reliable, scalable and cost-efficient. You’ll operate and improve Redshift, Databricks and Snowflake environments, help build out our data lake, and own performance tuning and production support. The role sits between data engineering and platform operations. You’ll investigate failures, automate repeatable work, and ship controlled changes through CI/CD and infrastructure-as-code.

📌 What you’ll do

• Operate and support Redshift, Databricks, Snowflake, PostgreSQL and MySQL.
• Build, maintain and troubleshoot data lake ingestion and processing pipelines.
• Investigate pipeline failures, slow queries, capacity limits and production incidents.
• Design and maintain data models, schemas and data movement patterns.
• Tune performance methodically: set a baseline, find the root cause, apply the smallest safe fix, then measure and document the result.
• Monitor platform health, reliability, compute and storage use, and costs.
• Create runbooks, dashboards and validation checks.
• Deploy changes through Git workflows, code review, automated tests, Jenkins and Argo CD, on Kubernetes and Terraform-managed infrastructure.
• Use AI tools to speed up SQL and code development, troubleshooting, documentation and analysis. Check AI output against tests, security policies and production-safe practices before using it.
• Follow security, privacy, access-control and change-management practices with production data.

⚙ What you bring

• Production experience operating data platforms, databases or pipelines.
• Strong SQL and a practical understanding of database performance.
• Experience with at least one cloud data warehouse and one data lake or distributed-processing platform.
• Data modeling knowledge: dimensional models, grain, keys and slowly changing dimensions.
• Familiarity with Git, CI/CD and infrastructure-as-code.
• Ability to troubleshoot across the application, database, data and infrastructure layers.