Data Engineer

Duration: 18months

NO C2C OR 1099

• Purpose of the Team:
  The purpose of this team is to provide telemetry reporting, analytics, automation, and AI-driven insights that improve visibility into service health, availability, and operational performance through dashboards, reporting solutions, and data engineering capabilities.
• Key projects:
  This role will contribute to telemetry and service health initiatives by developing reporting, analytics, and automation solutions. Responsibilities include building Power BI reports and Grafana dashboards, creating Kusto-based analytics, supporting telemetry monitoring, and improving data-driven insights related to availability and operational health.

• Disqualifiers:
  Candidates with limited or no experience in reporting and analytics, automation, AI-assisted solutions, data engineering, Power BI, Grafana, or Kusto-based data analysis will not be eligible for the role.
• Best vs. Average:
  The ideal resume will contain hands-on experience with telemetry and analytics platforms, including Power BI, Grafana, SQL/Kusto, Python, Power Shell, or C#, along with expertise in data engineering, ETL/data pipelines, automation, and AI-assisted reporting solutions. Experience supporting telemetry health, availability monitoring, operational dashboards, and enterprise-scale reporting environments is highly preferred.

• Expected Dates that Hard Skills Assessments will be scheduled: ASAP
• Hard Skills Assessment Process:
  The assessment process will include 1 round with sponsor.

The main function of an AI Data Engineer is to design, develop, enhance, validate, maintain, and support telemetry reporting, data solutions, automation, and AI-assisted solutions. The role develops reports, dashboards, data models, automation, reporting & analytics agent components, and supporting data solutions to improve telemetry coverage, telemetry availability, clean telemetry, and telemetry data quality.

Design, develop, and maintain telemetry reporting, analytics, automation, and AI-assisted solutions. Build Power BI reports and Grafana dashboards, develop Kusto queries and data models, integrate enterprise data sources, develop automation and reporting & analytics agent components, validate production solutions, and troubleshoot production issues.

Strong experience developing reporting, analytics, automation, and data solutions using Power BI, Grafana, SQL, Kusto or similar analytics platforms, Python, Power Shell or C#, data modeling, ETL/data pipelines, Azure Dev Ops, and AI-assisted reporting & analytics agent components. Experience with telemetry, monitoring, or operational systems is preferred.

• Reporting & Analytics — 5+ years:
  Power BI, Data Modeling, DAX, Grafana or similar reporting platforms, HTML/Web-based reports
• Automation, AI & Agentic Solutions — 3+ years:
  Python, C#, Power Shell, Agentic platforms / Copilot agents
• Data Engineering — 5+ years: SQL

• Design, develop, and maintain telemetry reporting, data solutions, automation, and AI-assisted solutions.
• Design, develop, and enhance Power BI reports, Grafana dashboards, semantic models, and analytics solutions to deliver actionable business insights.
• Develop and optimize Azure Data Explorer (Kusto/ADX) queries, functions, data models, scheduled tables, materialized tables, snapshot tables, and supporting datasets.
• Integrate enterprise data sources and develop supporting SQL queries, ETL processes, and data pipelines required for analytics and reporting solutions.
• Develop automation solutions using Python, Power Shell, C#, or similar scripting/programming languages to automate operational tasks and support business requirements.
• Develop AI-enabled reporting and analytics agent components, including Copilot Agents, Agency CLI skills, prompt assets, and supporting knowledge files.
• Validate data quality, business logic, report accuracy, dashboards, and scheduled refreshes to ensure reliable production solutions.
• Monitor, troubleshoot, and resolve data pipeline, query, reporting, and production issues to maintain solution reliability and performance.
• Create and maintain technical documentation, reusable components, and engineering best practices to support long‑term solution maintainability.
• Collaborate with cross‑functional engineering teams to deliver scalable, high‑quality analytics and reporting solutions.