Data Reliability Support Engineer

We are looking for an experienced Data Reliability Support Engineer to ensure data quality, reliability, observability, and compliance across enterprise data platforms. The candidate will develop automated validation frameworks, monitor data pipelines, enforce data contracts, and ensure SLA-compliant data reaches downstream platforms.

Key Responsibilities

• Design enterprise-wide data observability frameworks and monitor data platform health, freshness, volume, completeness, and schema changes.
• Develop automated anomaly detection, data validation, alerting, and incident management frameworks.
• Define, monitor, and improve data SLAs and SLOs.
• Prevent poor-quality or non-compliant data from entering downstream platforms through data quarantine and rejection mechanisms.
• Implement and enforce data contracts with Data Governance teams and upstream system owners.
• Integrate data quality metrics with governance platforms and support metadata, lineage, and data catalogue initiatives.
• Publish data quality scorecards and track SLA violations, recurring data defects, and reliability metrics.
• Lead root cause analysis, coordinate corrective actions, and escalate repeated data contract violations.
• Collaborate with Data Engineering, Data Governance, Platform, and source system teams to improve reliability and operational efficiency.

Required Skills & Qualifications

• 510+ years of experience in Data Engineering, Data Platform Engineering, or Reliability Engineering.
• Strong proficiency in SQL and Python.
• Hands-on experience with Grafana, Monte Carlo, or Acceldata.
• Experience with Informatica, Collibra, Talend Data Quality, or Microsoft Purview.
• Familiarity with Microsoft Azure, Azure Data Lake Storage (ADLS), Dynamics 365, and CRM systems.
• Experience in automated data validation, anomaly detection, data SLAs and SLOs, and data pipeline reliability.
• Strong understanding of distributed systems, enterprise data architecture, data contracts, metadata management, data lineage, and data governance.
• Familiarity with CI/CD pipelines and Infrastructure as Code (IaC).
• Strong analytical, troubleshooting, root cause analysis, stakeholder management, and cross-functional collaboration skills.
• Proactive problem-solving approach, adaptability, and commitment to continuous improvement.

Key Performance Indicators (KPIs)

• Reduction in downstream data incidents and recurring data defects.
• Increase in data sources covered by formal data contracts.
• Improvement in SLA adherence, data freshness, completeness, and overall quality metrics.
• Reduction in reactive troubleshooting.