T
BengaluruHigh payGCC
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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