Data Observability Engineer - Bigeye/Monte Carlo/Databand/Soda, Azure (Toronto)

Job Description Role: Big Eye Monitoring SME (Hybrid: 3 days in office; Duration: 7 months) Job Requirements

57+ years of experience in Data Engineering, Data Quality, Data Observability, or related disciplines. Hands‑on experience with Bigeye (preferred) or other observability platforms (e.g., Monte Carlo, Databand, Soda). Proficiency with Azure SQL, Azure networking concepts, authentication patterns, and identity management. Strong SQL skills and familiarity with data modeling practices. Experience designing and implementing CLI tools or automation workflows. Understanding of enterprise network security, including IP whitelisting, firewall behavior, and access control best practices. Experience working in Agile/Scrum environments and contributing to ADO‑based feature delivery. Key Responsibilities

Lead the design and implementation of a secure Azure SQL login flow tailored for Bigeye integrations. Define authentication requirements, service principals, credential handling, and connection policies. Collaborate with cloud platform and security teams to ensure compliance with Humana standards. Configure, validate, and operationalize initial data sources within the DEV‑DOP Bigeye workspace. Establish scalable onboarding patterns to support future additions of datasets and pipelines. Build templates or documentation to streamline future data source integration. Design and document the Bigeye CLI workflow, including command structures, automation patterns, and usage guidelines. Support engineering teams in implementing CLI‑based automation for data monitoring, alerting, and metadata management. Recommend enhancements to the CLI workflow, scripting, and DevOps integration points. Ensure Bigeye connectivity, crawlers, APIs, and ingestion services operate successfully within Humana’s IP‑whitelisted workplace. Partner with networking and security teams to validate inbound/outbound whitelisting requirements. Lead troubleshooting efforts for latency, access failures, or blocked traffic related to IP restrictions. Establish best practices for dataset monitoring, anomaly detection, SLA validation, and automated alert management. Create and maintain runbooks for onboarding, troubleshooting, and recurring operational tasks. Mentor engineering teams on data observability principles and Bigeye feature adoption. Collaborate closely with Data Engineering, Cloud Operations, Platform Security, and Analytics teams. Participate in Agile ceremonies, provide story estimates, and refine acceptance criteria for observability‑related features. Identify opportunities to enhance the data observability landscape, including automation, metadata capture, dashboards, and alert maturity. Stay current on Bigeye product updates and champion new capabilities across teams.

#J-18808-Ljbffr