Engineer - Target India

About us:

Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here.

About the Role

Join the Enterprise Collaboration Platform team to build backend services, data pipelines, analytics workflows, and automation capabilities that improve the health, reliability, and operational efficiency of collaboration platforms such as Zoom, Microsoft 365, Slack, and AI tools. The team’s mission is to convert fragmented telemetry into operational intelligence, improve reliability through engineering guardrails, and scale operational insights through automation.

Key Responsibilities

• Build scalable backend services and APIs for analytics and automation
• Develop telemetry ingestion, processing, and operational data models
• Implement reporting and insight-generation workflows for platform health and reliability
• Build automation to reduce manual operational effort and improve consistency
• Analyze telemetry to identify patterns, root causes, and improvement opportunities
• Partner with engineers and stakeholders across Endpoint, Security, and Device teams to improve digital employee experience

Required Qualifications

• Strong software engineering fundamentals
• Experience with backend development using Python or similar languages
• Solid SQL and data modeling fundamentals
• Experience building APIs, services, or data-processing systems
• Understanding of debugging, performance analysis, and operational problem-solving
• Ability to work in ambiguous environments and learn new domains quickly

Preferred Qualifications

• Exposure to observability, telemetry, monitoring, analytics, or SRE concepts
• Experience with operational dashboards, report generation, or data pipelines
• Familiarity with collaboration platforms such as M365, Slack, or Zoom
• Practical understanding of LLM applications, including:
  • choosing the right model for latency vs reasoning needs
  • token usage and context-window tradeoffs
  • prompt design and evaluation
  • cost implications of production-scale usage