Staff Engineer

Staff Engineer

Job Summary

The Staff Engineer is the senior individual contributor at Mindtickle. The role is defined by scope, not tenure or technology.

Staff Engineers shape technical direction across teams. They take on problems that cross system boundaries, i.e., foundational platform decisions, architectural work that spans multiple services, performance and reliability challenges that no single team owns, and AI capabilities that need to be woven into the product rather than bolted on.

This is not a senior engineer with a different title. The role exists for a different category of problem.

Key Responsibility

Solve Organization wide problems - crisply define the problem, propose the solution, implement it and get it adopted. One of the finest examples of this is the data platform at Mindtickle.

Take on problems that span teams — performance regressions across services, integration patterns that cut across domains, AI capabilities that have to hold up across varied customer configurations.

Review designs from other teams. Identify architectural risks before they become incidents. Propose and drive migrations, deprecations, and platform investments.

Set the technical standard through your work — the code you write, the design documents you publish, the reviews you give. Influence through clarity, not authority.

Be the champion of AI transformation at Mindtickle.  Evaluate where AI-native approaches should replace or augment traditional systems. Reasons about cost, latency, reliability, explainability, and maintenance burden. Help shape how the engineering organization itself uses AI tooling.

Own the operational health of the systems you influence. Drive reliability, observability, and performance work proactively, with data and clear proposals.

Make other engineers better — through design reviews, technical mentorship, and creating clarity in areas where others are stuck. Distribute knowledge rather than concentrate it.

Required Qualification

Clear evidence of significant industry experience as an individual contributor in software engineering, with the most recent years operating at Staff scope or equivalent. Scope is defined by the kind of problem owned, not the title held.

Demonstrated architectural ownership. Evidence that you have defined or significantly shaped system architecture in production that has stood the test of time — structural decisions you made, not designs you implemented.

Cross-team technical influence. Concrete examples where your work has shaped systems beyond your immediate team and thrived — patterns adopted by others, disagreements resolved across boundaries, design proposals taken up by other teams.

Production discipline. Direct ownership of systems running in production, with demonstrated work on reliability, observability, or operational improvement that survived contact with real customers.

Incident management. Track record of leading the response to production incidents — driving diagnosis under pressure, owning the post-incident review, and translating findings into structural improvements that prevent recurrence.

Strong written technical communication. Design documents, RFCs, or technical proposals that articulate complex trade-offs in writing.

Depth in at least one domain: multi-tenant SaaS systems, integration-heavy platforms, workflow or rules engines, configuration-driven architectures, real-time analytics, data pipelines, or platform infrastructure serving multiple internal teams.

Judgment on complexity. A record of making systems simpler — deprecations led, abstractions removed, boring technology chosen where it sufficed.

Preferred Qualification

9+ years of industry experience in software engineering, with multiple years operating at Staff scope or equivalent.

Experience leading large-scale migrations or platform transitions that affected multiple teams.

Familiarity with AWS and the architectural patterns relevant at scale — multi-region or multi-AZ design, managed data services, identity and security boundaries, cost-aware infrastructure choices.

Understanding of product metrics and product analytics — how systems are instrumented to support product decisions, how telemetry shapes prioritization, and how engineering choices surface in business outcomes.

Open-source contributions to systems-level projects — databases, infrastructure, runtime, build, observability — rather than feature contributions to consumer products.

Published technical writing — engineering blog posts, conference talks with substantive material, RFCs, design documents, and / or conference papers.

Experience in environments where customer variation is a first-class constraint — meaningfully different customer configurations, integrations, or data shapes within a single product line.

Familiarity with the broader Mindtickle problem space — sales enablement, revenue productivity, learning systems, content intelligence, or call analytics.

Experience Level