Motadata
Website:
motadata.com
Job details:
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
Click on Apply to know more.
