Data Engineer

In this role, you will strengthen data reliability across a large enterprise data platform, reducing incidents and improving data quality. You will collaborate with Data Engineering and Data Governance to design scalable observability and validation frameworks, ensuring data is accurate and trusted for critical decisions. You’ll own end-to-end data reliability, define SLAs/SLOs, and drive proactive issue prevention. This is a hands-on role in a hybrid, cloud-focused environment with a strong emphasis on collaboration and impact.

• Improve data quality, reduce incidents and build scalable data observability across the data platform
• Own data reliability end-to-end, design and implement health monitoring frameworks and anomaly detection
• Enforce data standards across complex data pipelines and platforms
• Define and manage data SLAs/SLOs; lead automated validation and observability tooling
• Lead root cause analysis when issues occur and drive proactive issue resolution
• Build automated data validation frameworks and support CI/CD and Infrastructure-as-Code initiatives
• Collaborate with Data Engineering and Data Governance to shift toward reliability-led operations
• Role requires proactive problem-solving and cross-functional collaboration

• Excellent SQL and Python skills
• Experience in modern cloud-based data environments
• Hands-on with data observability tools (Grafana, Monte Carlo, Acceldata)
• Experience with data governance/quality platforms (Informatica, Collibra, Microsoft Purview)
• Azure ecosystem experience (data lakes, ETL/ELT) would be advantageous
• Experience building automated data validation and working with distributed data architectures
• Familiarity with CI/CD and Infrastructure-as-Code is beneficial
• Proactive, collaborative problem-solver focused on root-cause improvements

• Proactive problem-solving
• Collaborative mindset
• Strong communication and cross-team collaboration
• SQL
• Python
• Cloud-based data environments