DataOps Engineer

Responsibilities

• Engineer the Data Lifecycle: Design and implement the "Golden Path" for data, ensuring seamless transitions between operational SQL environments, analytics warehouses, and AI-ready data sets.
• Implement Data as Code: Move beyond manual administration by treating AWS-hosted MS SQL infrastructure as a version-controlled, automated ecosystem using CI/CD and Infrastructure as Code (IaC).
• Architect Multi-Layer Reliability: Build frameworks that guarantee data quality and availability across all tiers—from high-concurrency operational databases to complex feature stores used by AI and Machine Learning models.
• Optimize for Scalability & Performance: Identify and resolve architectural bottlenecks in massive SQL Server environment, ensuring high-throughput demands of modern SaaS analytics.
• Standardize Data Observability: Develop advanced monitoring and alerting strategies for deep visibility into data health.
• Bridge the Engineering Gap: Collaborate with Software Engineers and Data Scientists to support rapid product iteration and long-term research initiatives.

Requirements

• 5+ years of experience in DevOps or Database Administration, with passion for migrating legacy data systems into modern, automated workflows.
• Deep expertise in managing MS SQL Server on AWS infrastructure (EC2, S3, CloudWatch).
• Proficient in Python, Bash, or PowerShell and working knowledge of C# for automation scripts.
• Outcome-oriented with ownership mindset.

Success Metrics

• Within 6 Months: Complete audit of data deployment processes and identify top three bottlenecks.
• Within 1 Year: Implement automated CI/CD for database schema changes, reducing deployment downtime.
• Long-term: Own "Data as Code" strategy for agile and resilient data layer.