I design and scale backend systems behind real-world products — from high-traffic consumer platforms to enterprise B2B learning solutions used at scale.
With 10+ years of experience, I specialize in system design, reliability, and engineering leadership. I work closely with product and business stakeholders to translate complex requirements into resilient, maintainable software that teams can operate confidently as systems grow.
Experience shaped by real systems, scale, and ownership
I am a Backend Engineering Manager with over a decade of experience building, scaling, and operating production systems across both consumer-facing and enterprise B2B platforms. My work has spanned high-traffic applications as well as correctness-critical systems used by large organizations.
I specialize in backend architecture using Node.js, system and API design, data modeling, and reliability engineering. I have designed systems that evolve over time, handle real-world failure scenarios, and remain maintainable as teams and requirements grow.
Beyond hands-on technical work, I lead engineering teams and take ownership of delivery outcomes. I work closely with product, design, and business stakeholders to translate ambiguous requirements into clear technical direction, while maintaining a strong engineering culture focused on quality, predictability, and learning.
I care deeply about building systems that teams can trust — systems that are observable, resilient, and designed with long-term sustainability in mind rather than short-term fixes.
What I optimize for: I optimize for systems that are reliable before they are clever, teams that can deliver predictably rather than heroically, and architectures that can evolve without constant rewrites. In practice, this means prioritizing clear ownership, simple interfaces, operational visibility, and decisions that reduce long-term cost even when they require short-term discipline.
What I deliberately avoid: I avoid unnecessary complexity, premature optimization, and architectures that are difficult to reason about or operate. I’m cautious of solutions that look elegant on paper but increase cognitive load for teams or hide operational risk. I also avoid relying on hero-driven execution, preferring systems and processes that allow teams to succeed sustainably.
The problems and systems I spend most of my time on
Designing APIs and backend services that handle real-world load, support growth, and remain stable under changing requirements.
Making architectural decisions around scalability, failure handling, data consistency, and long-term maintainability.
Building multi-tenant systems for enterprise learning, assessments, and personalized journeys with strong reliability guarantees.
Mentoring engineers, improving delivery processes, and aligning technical execution with business goals.
I treat engagement systems, analytics, and communication pipelines as first-class backend concerns, especially in learning platforms where outcomes matter more than feature counts. These principles are reflected in the systems I’ve built across consumer platforms like NEO and enterprise products such as Thriversity and IPJ, where real-world constraints shaped architectural and operational decisions.
My approach to system design is shaped by building and operating real production systems across both consumer and enterprise environments. I focus less on theoretical perfection and more on designs that work reliably under real-world constraints.
For learning platforms, system success is measured by learner completion, not just feature delivery. I design backend systems that actively support engagement through timely nudges, reminders, and behavioral signals.
I use event-based triggers to power email and notification workflows, enabling systems to react to learner behavior such as inactivity, progress milestones, or upcoming deadlines.
Beyond logs and metrics, I rely on product analytics to understand how users interact with systems. This feedback loop informs both technical decisions and product improvements.
I begin by understanding the business goal, expected usage patterns, and operational constraints. Scale, latency, and availability requirements guide architectural choices from the start.
Systems evolve. I prefer designs that allow iteration without large rewrites — clear API boundaries, modular services, and data models that can adapt as requirements change.
Simple, well-understood patterns are often more reliable than complex designs. I optimize for debuggability, predictable behavior, and operational clarity over unnecessary abstraction.
Failures are inevitable. I design systems assuming partial outages — with graceful degradation, clear error handling, and recovery strategies that reduce blast radius and mean time to recovery.
Especially in enterprise systems, correctness and consistency often matter more than raw throughput. I make deliberate trade-offs around consistency models and data ownership.
A system is only as good as its observability. I prioritize logging, metrics, and clear operational signals so teams can confidently operate and evolve the system.
Technologies, systems, and practices I actively use to design, build, and operate production-grade platforms
Designing and implementing scalable backend services with a focus on clean APIs, maintainability, and real-world performance.
Node.js, JavaScript, TypeScript, REST API Design, Express
Making architectural decisions that balance scalability, reliability, and long-term evolution under changing requirements.
Scalability, Failure & Recovery, API Boundaries, Data Ownership, Trade-offs
Modeling and managing data for both high-traffic consumer systems and correctness-focused enterprise platforms.
MongoDB, MySQL, BigQuery, Data Modeling, Consistency Considerations
Operating backend systems with predictable deployments, reproducible environments, and CI-driven workflows.
Docker, CI/CD (GitHub Actions), AWS (Foundational), GCP, Linux
Building backend-driven communication and nudging workflows to improve user engagement and completion outcomes.
SendGrid, Event-based Triggers, Notification Pipelines, Idempotent Messaging
Using analytics and behavioral signals to understand how users interact with systems and identify friction points.
CleverTap, Microsoft Clarity, Logs, Metrics, Behavioral Insights
Exploring data-driven enhancements and analytical workflows to support future intelligent features.
Python, Basic Machine Learning, Data Analysis
Leading teams, mentoring engineers, and aligning technical execution with business goals.
Technical Mentorship, Code Reviews, Cross-functional Collaboration, Delivery Ownership
Selected systems where I owned backend architecture, made system-level trade-offs, and delivered in production across both consumer and enterprise environments
A zero-to-one consumer PropTech platform built to surface pre-construction and under-construction property inventory — a segment often missing from traditional MLS systems.
I owned the backend architecture end-to-end, designing systems to handle highly variable listing data, performance-sensitive search, and fluctuating consumer traffic from first launch.
What this demonstrates:
Zero-to-one system design, real-world scalability,
and production ownership in a consumer marketplace.
Focus areas:
Backend architecture, search & discovery,
flexible data modeling, operational simplicity
A B2B self-paced enterprise learning platform focused on social, cognitive, and behavioral skills, designed to support large-scale adoption without cohort dependency.
Thriversity delivers a rich, skill-mapped content library with immersive learning experiences, while providing enterprise clients with deep visibility into engagement, usage, and learning health.
From an architecture perspective, the platform is optimized for read-heavy content consumption and discovery, with engagement features (certificates, goals, leaderboards) designed as shared platform capabilities rather than isolated features.
What this demonstrates:
Designing content-heavy enterprise platforms that balance
learner engagement, discoverability, and operational
control at scale.
Focus areas:
Content architecture, skill-based modeling, learner
engagement systems, enterprise reporting, and
AI-assisted discovery.
A configurable B2B enterprise learning platform built for upGrad Enterprise, enabling organizations to deliver cohort-based, outcome-driven leadership and professional skill programs at scale.
IPJ supports complex enterprise requirements including role-based access for learners, client SPOCs, and internal admins; structured learning journeys; automated engagement nudging; and deep partner reporting without impacting learner-facing performance.
Reporting workloads are intentionally decoupled from core learning flows — syncing data from MongoDB to GCP-backed stores on a scheduled basis to support enterprise analytics while preserving platform stability.
What this demonstrates:
Designing enterprise platforms with clear persona boundaries,
reporting isolation, and configuration-driven workflows that
scale across clients without increasing operational load.
Focus areas:
Multi-tenant backend design, role-based access control,
data architecture for reporting, automation, and
outcome-driven system modeling.
I’m open to conversations around backend architecture, system design, and engineering leadership — whether it’s a role, collaboration, or a technical discussion.
Email
sharewithmaurya@gmail.com
Mobile
+91 • 70429 • 01060
LinkedIn
linkedin.com/in/hemantkumarmaurya