Role - Senior Golang Engineer - Observability Preferred

Experience: 3+ Years

Location: Ahmedabad

Position Overview:

We are looking for a systems-minded developer with 3–5 years of hands-on experience who is comfortable

working close to the machine in Go/Rust, and equally comfortable reasoning about distributed behavior

across a cloud-native deployment. The ideal candidate has worked in the observability domain — metrics,

logs, traces, time-series storage and enjoys owning a module end to end. This is a high-ownership role in a

small, fast-moving team. You will work across multiple modules, make design calls, use AI-assisted

development as a daily tool, and be accountable for what you ship in production.

Role & Responsibility:

• Build and scale services for operational data ingestion, enrichment, storage, and query in a

multitenant SaaS environment — spanning push-based telemetry (metrics, logs, traces), polland protocol-based collection (SNMP, WMI, SSH, ICMP), event streams (traps, syslog, flow), and

discovery and inventory data.

• Work with event/message-driven pipelines (NATS) and time-series, relational, and cache

stores.

• Design ingestion paths that handle heterogeneous sources and cadences — high-volume

streaming, scheduled polling cycles, bursty event traffic, and transactional API writes — each

with different latency, ordering, and backpressure characteristics.

• Normalize collected data into a common internal model, applying OpenTelemetry semantic

conventions where they fit and defining consistent conventions where they don't, so

downstream consumers see one shape regardless of source.

• Build the data substrate for anomaly and signal detection — streaming aggregation, rollingwindow state, baselining, and correlation across metrics, logs, and traces — and integrate

model inference into the ingestion and query path within latency and resource budgets.

Own system design for your modules — write and defend design notes covering data

model, interfaces, scaling limits, failure modes, and rollback.

• Debug production problems end to end — reproduce, isolate, instrument, and fix issues

including performance regressions and platform-specific behavior.

• Use AI coding assistants and agentic workflows as part of the normal development loop,

while retaining full ownership of correctness, security, and design quality of the generated

output.

• Write clean, testable, well-instrumented code, participate in code reviews, and contribute

to design and engineering documentation.

• Collaborate with product managers, and fellow engineers to deliver high-quality features.

• Continuously learn and apply modern development practices and tools.

Skills and Qualifications:

• B.E./B.Tech in Computer Science, IT

• Minimum 3 years of hands-on backend engineering, with production-level proficiency in Go and the

ability and appetite to work in Rust.

• Demonstrable experience in the observability domain: metrics, logs, traces, agents, collectors,

monitoring systems, or telemetry pipelines.

• Working knowledge of cloud and distributed systems — containers, Kubernetes, service-to-service

communication, and the standard distributed failure modes (partial failure, backpressure, retries,

idempotency).

• Strong debugging and production problem-solving ability using profilers, tracing, logs, and metrics.

• Experience with OpenTelemetry, Prometheus or similar observability infrastructure.

• Strong reasoning and first-principles problem-solving — the ability to break down an unfamiliar

problem, form a hypothesis, and test it, rather than pattern-matching to a previous solution.

• Adaptive mindset — comfortable with changing priorities, evolving requirements, and switching

between languages, platforms, and modules.

• AI-ready working style — treats AI tooling as leverage, prompts and reviews critically, and

understands where generated code needs to be challenged.

• A strong reading and self-learning habit — follows source code, specifications, and engineering

blogs, and can bring what they read back into the product.

• Working comfort with statistical concepts as applied to time-series (distributions, seasonality,

percentiles, drift) and the ability to implement a detection algorithm from a specification or paper.

• Ownership and accountability — takes a module from design through production support, raises

risks early, and does not treat a handoff as the end of responsibility