Software Engineer Interview Prep Podcast

About Software Engineer Interview Prep Podcast

Ace your Software Engineer interviews with confidence. This podcast helps you organize your thinking, strengthen problem-solving skills, and prepare effectively for real technical interviews. Topics covered include: Programming (Java & Python) Data Structures & Algorithms System Design AI for Software Engineers Interview strategies & mindset Whether you're targeting Big Tech, startups, or senior engineering roles, each episode helps you think clearly, solve better, and perform at your best.

Publisher

Prabuddha Ganegoda

Category

education

Language

en

Explicit

No

First episode

17 Feb 2026

Latest episode

9 Oct 2026

Latest episodes

39 episodes in the feed.

1. 

   9 Oct 2026

   Kubernetes Deep Dive: Pod Startup and Failure Modes

   "Walk me through what happens from kubectl apply to the first request hitting your container." It sounds like a warm-up question, but how you answer it can decide whether you land a senior or staff role. In this deep dive we trace a pod from creation to production and unpack the failure modes interviewers expect you to anticipate. You'll learn: - The full startup chain: API server, etcd, controllers, scheduler, kubelet and container runtime - Why init containers can't run in parallel, and why migrations belong in one for security - Startup, liveness and readiness probes, and the liveness trap that turns a 10-second database blip into an outage - Native sidecars in Kubernetes 1.29 (KEP-753) and the startup and shutdown races they fix - HashiCorp Vault: mutating webhook injection, TokenReview and system:auth-delegator, and dynamic database credentials - Zero trust with mTLS, SPIFFE identities and JWTs, plus the alg: none attack and audience checks - Service DNS, ndots:5, ClusterIP "phantom" IPs, iptables vs IPVS, and headless services for gRPC - Istio traps: DestinationRule ordering, empty AuthorizationPolicies and a stray YAML hyphen - Helm secrets, migration hooks, and a five-phase rollout for breaking API changes - A layered runbook for debugging intermittent 503s Chapters 00:00 Introduction 00:22 What interviewers are really listening for 01:31 From kubectl apply to "success" 03:06 The scheduler and the kubelet 04:18 Init containers 05:26 Why migrations belong in an init container 06:34 Startup, liveness and readiness probes 08:09 The liveness probe thundering herd trap 10:04 What a liveness probe should check 10:53 Startup probes for slow-booting apps 11:40 The sidecar startup and shutdown race 12:52 Native sidecars in Kubernetes 1.29 13:39 Vault injection with a mutating webhook 14:48 Vault's init and sidecar agents 15:34 Vault authentication and TokenReview 16:43 Dynamic database credentials 17:55 A Postgres quoting trap 18:44 Zero trust and mutual TLS 19:52 SPIFFE identities in Istio policies 20:17 mTLS vs JWT 20:39 Istio policy traps 21:46 The alg: none attack and audience checks 23:20 Service DNS and the phantom ClusterIP 24:31 iptables vs IPVS and headless services 25:41 Istio routing and deployment order 26:29 The YAML hyphen trap 26:53 Helm versions and secrets 27:41 Helm migration hooks 28:04 Five phases for a breaking API change 29:14 Debugging 503s step by step 30:00 Matching symptoms to causes 30:47 Final thought: abstractions leak #Kubernetes #DevOps #SRE #CloudNative #Istio #SystemDesign #InterviewPrep Interview Prep Podcast

2. 

   3 Oct 2026

   Cracking the Senior System Design Interview: RADIO, NFRs, Scale and CAP

   Senior system design interviews don't test whether you know what a load balancer is. They test how you think under pressure: how you handle ambiguity, weigh brutal trade-offs and defend every box you draw. In this deep dive we unpack the framework used to evaluate senior engineers, architects and principal candidates. You'll learn: - The RADIO framework (Requirements, API, Data model, Infrastructure, Optimize) and how to split your 45 minutes - The one question that instantly signals seniority - REST vs gRPC, API versioning, and why offset pagination breaks at scale - Choosing a database by access pattern, and justifying every component with the NFRs - Why skipping non-functional requirements is the number one reason senior candidates fail - Peak vs average TPS, P99 tail latency, latency budgets, RPO and RTO, and compliance - Sticky sessions, sharding, hot shards, read replicas, CQRS and event streaming - The math of the nines, active-active vs active-passive, circuit breakers, bulkheads and graceful degradation - Exponential backoff with jitter to survive the thundering herd - The CAP theorem, the consistency spectrum, and why banks must choose CP to prevent double spending Chapters 00:00 Introduction 00:49 They test judgement, not definitions 02:22 The roadmap 03:36 The RADIO framework 04:47 Requirements in five minutes 06:00 The seniority signal 06:47 API design and versioning 08:20 Offset vs cursor pagination 09:05 Choosing the data model 10:18 Justifying infrastructure with NFRs 11:28 Network boundaries 11:50 Attacking your own design 12:39 Observability and cost 14:37 The URL shortener trap 15:23 The NFR cheat sheet 15:46 Peak vs average traffic 16:33 Tail latency and fan-out 17:44 Latency budgets 19:16 RPO and RTO 20:02 Compliance: GDPR, PCI DSS, SOX 21:37 Scaling out and sticky sessions 23:13 Sharding and the hot shard 24:49 Cross-shard query trade-offs 25:58 Read replicas and replication lag 27:08 CQRS 28:41 Event streaming and idempotency 31:02 The nines of availability 33:04 Active-active vs active-passive 34:37 Circuit breakers 36:09 Bulkheads 37:18 Graceful degradation 38:26 Thundering herd, backoff and jitter 40:25 The CAP theorem 43:09 The consistency spectrum 45:32 The FinTech exception: double spending 47:05 Why a rejected transaction beats a duplicate 48:13 Recap: every design is a trade-off 49:23 Final thought #SystemDesign #SoftwareEngineering #DistributedSystems #InterviewPrep #TechInterviews #SoftwareArchitecture Interview Prep Podcast

3. 

   3 Oct 2026

   Kafka in Production: Failure Scenarios for Staff-Level Interviews

   A single misconfigured setting in Kafka doesn't just slow a page down. It can silently vaporize financial transactions. In this deep dive we walk through real production failure scenarios and the exact reasoning interviewers look for in staff-level system design interviews. You'll learn: - Why acks=1 loses data, and the four configuration layers for zero data loss - Consistency vs availability: why a healthy cluster refuses writes on purpose - Pushing from 400,000 to 2 million events/s with batching, linger.ms and compression - How retries reorder a debit and a credit, and how idempotent producers fix it - Rebalance storms, cooperative sticky assignment and static membership - Hot partitions and the irreducible trade-off between ordering and scale - How deleting and recreating a topic silently skips hours of data - A 3 AM role-play: 85 under-replicated partitions, a disk at 96%, and why you never restart the broker - Exactly-once with Kafka transactions, and exactly-once into PostgreSQL - Tiered retry topics and dead letter queues - Split brain, KRaft quorums and vanishing tombstones - System design at scale: multi-tenant topics, quotas, event-driven sagas and change data capture with Debezium Ideal for backend and platform engineers preparing for senior and staff-level interviews. Chapters 00:00 Distributed systems have no X-ray 01:08 Why Kafka interviews matter 02:19 acks=1 and the 200 missing payments 03:27 Four layers of zero data loss 05:21 The CAP theorem trade-off 06:31 Throughput: taxis vs buses 07:42 batch.size, linger.ms and compression 09:38 When retries reorder messages 11:38 Idempotent producers 12:48 Message keys and partition ordering 13:12 Flash sale: buffer exhausted 14:22 Decoupling with a local buffer and circuit breaker 16:18 Rebalance storms 18:13 Eager vs cooperative sticky rebalancing 19:25 Static group membership 20:12 Hot partitions and the hot key problem 23:18 Silent data loss after recreating a topic 25:20 auto.offset.reset and topic versioning 26:08 3 AM: 85 under-replicated partitions 28:54 Why you never restart the broker 29:42 Disk at 96%: the 15-minute response 31:36 Throttled reassignment, Cruise Control, tiered storage 33:36 Duplicates in consume-transform-produce 34:47 Kafka transactions 37:11 Exactly-once into PostgreSQL 39:56 Head-of-line blocking from retries 41:08 Tiered retry topics and DLQs 42:42 Split brain 44:38 KRaft and quorum math 45:27 Log compaction tombstones 48:33 Multi-tenant topic design 50:07 Tenant tiers and client quotas 51:40 Event-driven sagas 53:15 Compensating transactions 54:51 Change data capture with Debezium 57:13 Recap 58:46 Will offsets still matter in five years? #Kafka #SystemDesign #DistributedSystems #SoftwareEngineering #InterviewPrep #BackendEngineering Interview Prep Podcast

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