“Included as a consequential modern Indian data platform employer with relevant product, engineering, data or AI roles.”
4
stages
2–5 weeks
end to end
20
practice questions
Hiring now
Atlan is hiring right now
landed applies to roles like these for you, on each company’s own site, with answers written for the role.
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01
The interview, stage by stage
- 1
Founder or hiring manager screen60 min
data architecturegovernancePrepare one quantified example demonstrating data architecture.
- 2
Practical role exercise60 min
governancereliabilityPrepare one quantified example demonstrating governance.
- 3
Technical/product deep dive60 min
reliabilitydeveloper experiencePrepare one quantified example demonstrating reliability.
- 4
Team values and ownership round90 min
developer experienceenterprise adoptionPrepare one quantified example demonstrating developer experience.
02
What decides the offer
Data architecture
25%
Shows specific decisions and measurable evidence for data architecture.
Governance
20%
Shows specific decisions and measurable evidence for governance.
Reliability
20%
Shows specific decisions and measurable evidence for reliability.
Developer experience
20%
Shows specific decisions and measurable evidence for developer experience.
Enterprise adoption
15%
Shows specific decisions and measurable evidence for enterprise adoption.
They look hardest for data architecture, governance, reliability, developer experience.
03
Your four weeks
Days 1–2
Company and market
- ·Map products, users, revenue model and competitors
- ·Write a one-page company thesis
Days 3–5
Core role skills
- ·Practice data architecture
- ·Practice governance
- ·Practice reliability
Days 6–8
Company-context cases
- ·Design a governed metadata and data-discovery platform.
- ·Handle lineage across fragmented enterprise systems.
- ·Add AI search without leaking restricted data.
Days 9–11
Technical simulations
- ·Complete two timed exercises
- ·Run one architecture/product mock
- ·Practice follow-up pressure
Days 12–14
Stories and final loop
- ·Prepare six quantified ownership stories
- ·Rehearse project deep dive
- ·Prepare interviewer questions
04
Practice these
- How would you design an AI assistant for doctors?Recommended
- How would you launch an AI feature whose output cannot always be objectively graded?Recommended
- Design a production RAG system for ten million documents.Recommended
- How would you defend an LLM application against prompt injection?Recommended
- Design an agent that can safely call external tools.Recommended
- Offline evaluations improved but production metrics fell. What happened?Recommended
- Compare supervised fine-tuning, preference optimization, and reinforcement learning from feedback.Recommended
- How would you design multi-tenant retrieval without leaking customer data?Recommended
- Design a batching service for model inference.Recommended
- Design an account-research and personalization pipeline for 10,000 prospects.Recommended
- Design reliable synchronization between a CRM, data warehouse, and enrichment providers.Recommended
- Design an enterprise architecture for using multiple model providers.Recommended
- How do you investigate a production incident involving unsafe model output?Recommended
- How should a team set release thresholds when safety metrics have uncertainty?Recommended
- How would you audit whether an AI system treats demographic groups fairly?Recommended
- Design a model router that balances quality, latency, and cost.Recommended
- How would you build a feedback loop without amplifying user bias or abuse?Recommended
- Design an AI coding assistant for a large enterprise codebase.Recommended
- Design memory for a long-running personal AI assistant.Recommended
- A model refuses too often after a safety update. How do you diagnose and fix it?Recommended
05
Where people slip
- !Generic enthusiasm about startup culture or AI
- !No understanding of the company business model
- !Ignoring Indian price sensitivity, regulation or operational variance
- !Architecture without failure recovery and observability
- !Claiming a recommended case was actually asked
06
Ask them this
- ?What is the exact loop for this team and level?
- ?Which rounds permit AI tools?
- ?What would I own in the first 90 days?
- ?What is the hardest product or model constraint the team faces in India?
- ?How does the team measure quality after launch?
- ?How are ESOPs valued and what is the exercise policy?