round 1:
- What data issues have you run into during backtesting?
- How do you do identifier resolution?
- How do you choose among multiple vendor sources and backfill between them?
- How do you handle stale data, missing data, and outliers?
- How do you avoid look-ahead bias in data like earnings estimates?
- If backtest results suddenly change, how do you determine whether it's a model, code, or data problem?
- What components does the data pipeline use, and how is it orchestrated?
- Why did you choose Jain?
round 2: Data Discovery / User Support
Scenarios included:
- An analyst asks where to find Bloomberg company IDs and the corresponding figures.
- If there's no mature data catalog, how do you help them locate it?
- Should the user read data through Excel, API, SQL, or the platform?
- How do you distinguish between an analyst persona and a developer persona?
- How do you alert when a data refresh fails?
- How do you explain licensing, entitlement, and lineage?
- How do you support self-service use while preventing users from directly using unvalidated raw vendor data?
round 3: Compliance Data Modeling Case
This was the most clearly structured case round:
- The security master has company, country, and security/FIGI.
- Compliance restrictions can target company, country, or a combination.
- Restrictions have effective dates.
- How would you design the schema?
- How do you map a restriction to all affected securities?
- What do you do if a given version of the security master doesn't contain a company referenced by a restriction?
- How do you handle ticker changes and security master freshness?
- During a system migration, how do you verify that the old and new systems produce consistent output?
- How do you test for missing references, overlapping date ranges, and special combination rules?
This round tested:
- relational modeling
- temporal data
- referential integrity
- versioning
- auditability
- financial compliance judgment
Discussion
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