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System Design for Senior Engineers
Design distributed systems that survive millions of users, failures, and midnight on-calls.
CREATED BY
M
Manu V. [PLACEHOLDER] ★ 5.0
Senior Software Engineer at SoftGiant | 16+ years of experience
About this Path
Built for engineers targeting Staff, Principal, or Senior SWE roles where system design is the make-or-break round. You will move from whiteboard vagueness to structured, interviewer-ready designs for real-world platforms. Every module is grounded in production trade-offs, not textbook theory.
Path Overview
Advanced LevelCertificate of CompletionAbout 60 hours to completeEnglish language20+ curated videosLearn online at your own pace6 modules with resourcesGamified & interactive
Path Curriculum
The 45-minute design interview playbook
Scope clarification, functional vs non-functional requirements, and time-boxing each phase.
Capacity estimation & back-of-the-envelope math
QPS, storage, bandwidth estimates using realistic multipliers and order-of-magnitude thinking.
API design: REST, gRPC, and GraphQL trade-offs
Choosing the right protocol, versioning strategies, and pagination patterns.
Data modelling: SQL vs NoSQL decision matrix
When to denormalize, pick DynamoDB, Cassandra, or Postgres for a given access pattern.
Horizontal scaling and stateless services
Session externalization, sticky sessions pitfalls, and load-balancer algorithms.
Consistent hashing and partitioning strategies
Virtual nodes, hotspot mitigation, and resharding without downtime.
Replication: leader-follower, multi-master, quorums
Read replicas, replication lag, and linearizability guarantees in practice.
Rate limiting patterns: token bucket vs leaky bucket
Distributed rate limiters using Redis sorted sets and sliding window counters.
Cache strategies: aside, write-through, write-behind
Invalidation, stampede prevention, and choosing TTL vs event-driven expiry.
Redis deep-dive: data structures for system design
Sorted sets for leaderboards, Bloom filters, pub/sub, and Lua scripting.
CDN architecture and edge caching patterns
Cache-Control headers, purge strategies, and serving dynamic content at the edge.
Kafka fundamentals for system design interviews
Topics, partitions, consumer groups, offsets, and exactly-once semantics.
Event sourcing and CQRS in distributed systems
Append-only logs, projections, and rebuilding read models from event streams.
Saga pattern for distributed transactions
Choreography vs orchestration, compensating transactions, and failure rollback flows.
Change data capture with Debezium and outbox pattern
Reliable dual writes, transactional outbox, and downstream fan-out at scale.
Object storage design: S3-like systems internals
Chunking, checksums, metadata servers, and eventual consistency semantics.
Full-text search with Elasticsearch and inverted indexes
Sharding strategy, relevance scoring, and keeping search in sync with primary DB.
Time-series databases for metrics and monitoring
InfluxDB / Prometheus data model, downsampling, retention, and cardinality limits.
Circuit breakers, retries, and timeout budgets
Exponential backoff with jitter, bulkhead isolation, and failure cascade prevention.
Distributed tracing with OpenTelemetry
Trace propagation, sampling strategies, and correlating logs, metrics, and traces.
Designing URL shorteners, feeds, and chat systems end-to-end
Full walkthroughs of three canonical interview designs with trade-off commentary.
Mock design interview: live feedback & scoring rubric
Recorded walkthroughs critiqued against clarity, depth, and trade-off articulation.
What you'll learn
- ✓Design horizontally scalable services using consistent hashing, sharding, and replication strategies.
- ✓Evaluate CAP theorem trade-offs and choose the right consistency model for a given workload.
- ✓Architect event-driven systems using Kafka, change-data-capture, and idempotent consumers.
- ✓Build reliable read and write paths with caching layers, CDNs, and database indexing patterns.
- ✓Communicate designs clearly in interviews using a repeatable framework covering scope, API, data model, and scaling.
- ✓Conduct capacity estimation and back-of-the-envelope math with confidence under interview pressure.