Your First Platform Design
Skip the theory. A startup hands you a brief — turn it into a structured requirements doc in 5 minutes (QPS, latency, storage, retention, freshness, blast radius) before you pick a single technology.
Phases
3
LeadershipExpert only
System Design for Data Engineers
Architect data platforms that survive scale, on-call rotations, and senior+ interviews — capacity, partitioning, failure modes, and the rubric panels actually score against.
System design is the load-bearing skill from senior to staff. Every senior+ interview tests it; every on-call incident exposes who has it. This curriculum is built around real platform-failure incidents, not whiteboard puzzles.
What you'll do
What you'll be able to do.
- Deconstruct any data platform into ingestion / storage / serving / ops
- Make explicit tradeoffs across cost, latency, reliability, and complexity
- Diagnose why a platform broke at scale, and propose a defensible redesign
- Pass senior+ system-design interviews with the rubric panels actually score against
Phase roadmap.
Phase 1EXPERT REQUIRED
Understand & Deconstruct Systems
What a data platform actually is — the requirements doc, the failure modes that decide its shape, and the architecture patterns you can swap between when one stops working.
1.1
⊘Your First Platform Design
Skip the theory. A startup hands you a brief — turn it into a structured requirements doc in 5 minutes (QPS, latency, storage, retention, freshness, blast radius) before you pick a single technology.
1.2
⊘Platform Foundations
What broke when your data system stopped scaling — compute vs IO vs coordination bottlenecks, partitioning lessons, hot-shard diagnosis, and the foundations every staff engineer references when a platform falls over.
1.3
⊘Architecture Patterns
Lambda vs Kappa, medallion, event-driven, request-response, batch+stream lambda — when each fits, when each fails, and the pattern-swap exercise that turns 'we should use Kafka' into a defensible decision.
Phase 2EXPERT REQUIRED
Design & Scale
The three layers every data platform has — ingestion, storage, serving — sized with capacity math and chosen against named tradeoffs, not vibes.
2.1
⊘Ingestion & Processing
Your pipeline just failed at 2AM — the diagnostic ladder. Push vs pull, exactly-once semantics, ordering guarantees, batch vs stream cutoffs, and the capacity math that picks the ingestion topology before you commit to a tool.
2.2
⊘Storage Architecture
Size storage cost before you pick a tool. Partitioning, clustering, hot/warm/cold tiering, OLTP vs OLAP vs lake vs lakehouse tradeoffs, and the cost-vs-latency curve that decides Iceberg vs Delta vs raw Parquet.
2.3
⊘Serving & Analytics
Redesign the serving layer. OLAP cubes vs materialized views vs query engines, semantic layer placement, caching tiers, BI vs ML vs operational query patterns, and the freshness-vs-cost knob that decides every architecture above.
Phase 3EXPERT REQUIRED
Master & Ship
What separates a senior architect from a staff one — running platforms on-call, defending designs in interviews, extending architecture for the AI era, and the capstone that puts it all together.
3.1
⊘Production Operations
Design for incident response — SLO modeling, blast-radius isolation, capacity headroom, on-call runbooks, blameless postmortems, and the design choices that decide whether 2AM pages are debuggable or hopeless.
3.2
⊘Interview Mastery
Decode the panel rubric — the 5 axes senior+ panels actually score against (clarification, capacity, decomposition, tradeoffs, communication), the 35-minute structured walkthrough, and the level-vs-staff signal that decides your offer.
3.3
⊘AI Data Platforms
Why AI changes data architecture — the 7-layer AI-native platform (vs the 4-layer pre-2023 stack), feature stores vs vector stores, model-data lineage, and the new failure modes (training/serving skew, embedding drift) that didn't exist in 2022.
3.4
⊘System Design Capstone
TechCorp's platform is broken at 500GB/day after working fine at 5GB/day — 5-step end-to-end redesign with capacity math, architecture proposal, migration plan, runbook, and an executive presentation defended against a CTO panel.
What you'll ship
What you'll build.
- Capacity-estimation worksheet (QPS, storage, bandwidth, cost per ingestion stage)
- Reference architecture: ingestion → storage → serving with named tradeoffs
- Incident-response playbook: failure modes, blast radius, runbooks
- Interview-grade design walkthrough scored against a real panel rubric
Definition
What is System Design?
System design for data engineers is the practice of architecting scalable data platforms — designing ingestion, processing, storage, and serving layers that handle terabytes of data reliably. It covers distributed systems principles, architecture tradeoffs, and the structured frameworks used in senior-level interviews at companies like Google, Meta, and Netflix.
Production context
Why this matters in production.
Every data platform is a system design challenge. At Netflix, the data platform team designs systems that ingest billions of events, process petabytes daily, and serve analytics to thousands of users. Production system design requires balancing cost, latency, reliability, and scalability — tradeoffs that define senior engineering work.
Use cases
Common use cases.
- Designing end-to-end data platform architectures with ingestion, processing, and serving
- Making tradeoff decisions between cost, latency, reliability, and complexity
- Architecting storage layers with proper partitioning, caching, and tiering
- Designing streaming and batch processing systems for different latency requirements
- Preparing for system design interviews at senior and staff engineer level
- Evaluating and selecting technologies for production data platforms
Compare
System vs alternatives.
SystemvsLeetCode-style prep
Coding tests implementation skills. System design tests architecture and tradeoff thinking — and senior+ panels weight it heavier because architecture decisions have larger blast radius than individual code. This curriculum drills the rubric, not algorithms.
SystemvsDDIA reading
Designing Data-Intensive Applications is the reference. This curriculum is the practice reps — incident-style scenarios, capacity math worksheets, panel-rubric walkthroughs, and a TechCorp-style capstone. Read DDIA for theory; do this for muscle memory.
SystemvsGeneric SWE system design
Generic system-design content is web-app heavy (URL shorteners, chat apps). Data-engineering interviews test ingestion topology, storage tradeoffs, OLAP serving, and pipeline failure modes — patterns that don't show up in a Twitter-clone walkthrough. This curriculum is built for that panel.
Before you start
Before you start.
Tech stack
- Architecture
- Distributed Systems
- Storage Tradeoffs
- Scale
Prerequisites
- 2+ years data engineering experience
- Familiarity with distributed systems
Why this matters
Why this skill matters.
System design is what gets you promoted from senior to staff. This curriculum proves you can architect a data platform, defend the tradeoffs in front of a panel, and run it on-call when something breaks at 2AM.
FAQ
Common questions about System.
System Design for Data EngineersUpgrade to Expert