| Minimum Requirements:
Candidates that do not meet or exceed the minimum stated requirements (skills/experience) will be displayed to customers but may not be chosen for this opportunity. |
| Years | Required/Preferred | Experience |
| 8 | Required | Multi Cloud Platform Expertise (Azure + Google Cloud Platform) |
| 8 | Required | GenAI & Enterprise AI Platform Knowledge (Gemini, Vertex AI) |
| 8 | Required | Expertise in Azure Monitor, Log Analytics, Google Cloud Platform Cloud Monitoring, and logging frameworks to track performance, reliability, and usage. |
| 8 | Required | Experience using Logstash for log ingestion, transformation, and integration with Azure Log Analytics for proactive monitoring and alerting |
| 8 | Required | Ability to design alerting solutions using Twilio (SMS/voice) for ETL failures and operational notifications. |
| 8 | Required | Experience with Blob Storage, Data Lakes, and structured storage systems enabling analytics and AI workloads. |
| 8 | Required | Hands-on experience supporting data pipelines, data platforms, and analytics workloads in enterprise environments |
| 8 | Required | Experience with Informatica Cloud (IICS), Secure Agents, APIs, and integration patterns, including handling monitoring and integration challenges. |
| 8 | Required | Strong implementation of IAM (least privilege), network security, audit logging, and compliance (FedRAMP/regulated env.). |
| 8 | Required | Ability to support Tier 2/3 issues, debug logs, resolve platform access issues, and maintain stability in production environments |
| 3 | Preferred | Familiarity building and managing Tableau Cloud environments |
| 3 | Preferred | Experience integrating ArcGIS or geospatial data systems with cloud data platforms. |
| 3 | Preferred | Knowledge of scripting (Python, Bash) and automation (serverless, CI/CD pipelines). [ | Outlook] |
| 3 | Preferred | Exposure to GKE, AKS, Docker, and microservices architectures. |
| 3 | Preferred | Familiarity with data mesh, ETL/ELT architectures, API integrations, and enterprise data standards. |
| 3 | Preferred | Strong analytical thinking to assess platform issues, make timely architectural decisions, and drive resolution in high-pressure environments. |
| 3 | Preferred | Ability to take end-to-end ownership of platform stability, reliability, and delivery, proactively identifying risks and driving outcomes |
| 2 | Preferred | Ability to clearly translate complex technical concepts into business-friendly language and actively engage with stakeholders, leadership, and end users |
| 2 | Preferred | Proven ability to lead and coordinate across engineering, data, infrastructure, and business teams, ensuring alignment and delivery of platform initiatives. |
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