Sr Engineer

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.

As a Senior Engineer, you will lead the design and development of platform-level capabilities for operational intelligence, reliability engineering, and automation across enterprise collaboration ecosystems. You will work on telemetry-driven systems that identify what is unhealthy, why it is happening, and what should be done next — while building automation and architecture that scales across multiple platforms and partners

Key Responsibilities

• Own the design and delivery of critical backend and analytics platform components
• Build scalable telemetry, analytics, and automation systems
• Define operational metrics, health signals, and reliability indicators
• Drive platform evolution from provider-specific analytics to reusable cross-platform capabilities
• Enable reliable operations through engineering guardrails, automation, and data-backed workflows
• Partner cross-functionally with Endpoint Engineering, Device Management, Security, and collaboration platform stakeholders
• Mentor other engineers and raise technical quality across the team

Required Qualifications

• Strong backend engineering experience in Python or similar technologies
• Deep SQL, data processing, and systems design experience
• Experience designing scalable services, APIs, or data-intensive platforms
• Strong problem-solving ability across complex operational systems
• Experience dealing with ambiguity and translating business/operational problems into engineering solutions

Preferred Qualifications

• Experience in observability, telemetry platforms, SRE, reliability engineering, or operational analytics
• Experience with anomaly detection, performance monitoring, and report automation
• Familiarity with enterprise collaboration platforms and their operational signals
• Strong practical understanding of LLM-enabled systems, including:
  • model selection for latency vs reasoning
  • token/cost optimization
  • prompt and response architecture
  • evaluation and safety considerations
  • production design patterns for AI-assisted workflows