Sr Engineer

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