Role & responsibilities
Production Support: Address and resolve production incidents as per SLA, including user troubleshooting.
Data Pipeline Monitoring: Monitor pipelines, detect failures, and implement corrective actions to maintain uninterrupted data flow.
Model Performance Oversight: Track drift, latency, and accuracy of deployed models; apply corrections and ensure retraining cycles do not disrupt service.
Dashboard & Data Accuracy: Manage dashboard refreshes and resolve data accuracy issues.
Service Requests: Handle and resolve service requests with efficiency and timeliness.

Preferred candidate profile
Experience: 4+ years of relevant industry experience in ML/AI production systems, with at least 2 years in Data Engineering and Power BI
Data Engineering: Developed and optimized ETL pipelines using Apache Spark and SQL to process large-scale datasets for downstream machine learning models.
Reporting & Analytics: Designed and delivered interactive Power BI dashboards that enabled product teams to monitor data-quality metrics and key performance indicators (KPIs) for deployed models

📌 Senior System Engineer Power Bi + Azure Databricks Bengaluru (India)
🏢 Tiger Analytics
📍 India