I started drawing out the entities and immediately second-guessed whether Positions should be a derived entity or a first-class table.
Start by clarifying the business context and key requirements (e.g., trading frequency, limit types) to scope the model. Then, walk through each entity, its attributes, and relationships, using an ERD to visualize primary/foreign keys and cardinalities. Conclude by discussing trade-offs and how the model supports analytical needs.
Pro tip: Emphasize that the model should balance normalization for data integrity with denormalization for query performance, especially for time-series data like trades and positions. Mention that in financial services, auditability and temporal tracking are often critical, so consider adding valid-from/valid-to dates or versioning.
Ask about the business domain: Is this for a bank, hedge fund, or retail brokerage? What are the key use cases (risk, reporting, trading)? This determines the level of detail and whether to include historical tracking.
List each entity (Customer, Account, Trade, Position, Limit) and its key attributes. For example, Customer has customer_id, name, type; Trade has trade_id, trade_date, quantity, price, side.
Determine how entities relate: A Customer can have multiple Accounts (1:N). An Account can have multiple Trades (1:N). A Trade results in a Position update (N:1 to Position? Actually, Position is derived from Trades, so it's a many-to-one from Trade to Position if Position is per instrument per account). Limits are associated with Accounts or Customers (1:N).
Assign primary keys (e.g., customer_id, account_id, trade_id) and foreign keys (e.g., account.customer_id references customer.customer_id). Consider composite keys for junction tables if needed (e.g., for many-to-many between Limits and Accounts).
Talk about normalization vs. performance, handling historical data (slowly changing dimensions), and potential extensions like adding a Product or Instrument entity. Mention how the model supports analytical queries (e.g., aggregating trades to positions).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business context and query patterns, then evaluate normalization vs. denormalization tradeoffs based on performance, storage, and maintainability. Recommend a balanced approach, such as a star schema with selective denormalization for critical analytics, and justify with concrete examples.
Pro tip: Always tie your recommendation to the specific use case and SLAs—interviewers at EY value client-focused pragmatism over dogmatic adherence to one approach. Mention that denormalization can reduce join complexity but may increase data redundancy and update anomalies, so it's a tradeoff.
Ask about the intended use cases, query patterns, data volume, and performance expectations to ground your analysis.
Highlight how normalization reduces redundancy, ensures data integrity, and simplifies updates, which is crucial for transactional systems.
Explain how denormalization improves read performance, simplifies queries, and is beneficial for analytical workloads with complex joins.
Recommend a design that balances both, such as a star schema with dimension denormalization, and justify based on the tradeoffs.
Mention maintenance overhead, data refresh strategies, and how to monitor performance to validate the chosen approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
SCD type 2 was my immediate answer and I walked through adding effective date and expiry date columns plus a current flag.
Start by defining slowly changing dimensions (SCDs) and their importance in data warehousing. Then, compare SCD types (1, 2, 3, 4, 6) with a focus on type 2 as the most common for customer data, and discuss trade-offs like storage, query complexity, and historical accuracy. Finally, recommend a specific approach based on business needs, such as using type 2 for tracking customer attribute changes over time.
Pro tip: Emphasize that the choice of SCD type should align with business requirements and analytics use cases, not just technical preference. Mention that in practice, a hybrid approach (e.g., type 6) often balances history and performance.
Explain what slowly changing dimensions are and why they matter for maintaining historical accuracy in a data warehouse, especially for customer data.
Briefly describe SCD types 1, 2, 3, 4, and 6, highlighting their pros and cons in terms of history preservation, storage, and query complexity.
Propose a specific SCD type (e.g., type 2) for the Customers entity, justifying it with business needs like tracking customer attribute changes over time.
Outline how to implement the chosen SCD type, including ETL processes, surrogate keys, and handling late-arriving data, and discuss trade-offs such as increased storage and query complexity.
Conclude by emphasizing that the choice should be driven by business requirements, such as regulatory compliance or customer analytics, and mention potential hybrid approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Star schema vs lakehouse is a debate I've had at actual jobs so this felt more natural.
Start by clarifying the business context and consumer needs, then describe how you would expose curated datasets through a semantic layer or governed views. Compare star schema and data lakehouse across dimensions like performance, flexibility, cost, and governance, and conclude with a recommendation tailored to the scenario.
Pro tip: Emphasize that the choice depends on use cases: star schema for BI and SQL analytics, lakehouse for ML and diverse data. Mention that a hybrid approach (lakehouse with star schemas on top) is often pragmatic in consulting.
Ask about the consumers (BI analysts, data scientists), data volume, variety, latency, and governance needs. This ensures your answer is context-driven.
Describe how you would expose curated datasets: e.g., through a semantic layer, governed views, or APIs. Highlight the importance of documentation, access control, and discoverability.
Contrast star schema and data lakehouse on key tradeoffs: performance, flexibility, cost, scalability, governance, and support for advanced analytics.
Provide a recommendation based on the context, possibly suggesting a hybrid approach where the lakehouse stores raw and curated data, and star schemas are built for BI.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structured this as three separate paths off a shared curated layer.
Start by clarifying the distinct requirements of each consumer—latency, data freshness, query complexity, and governance—then propose a tailored read path for each, such as pre-aggregated tables for dashboards, immutable snapshots for regulatory extracts, and a flexible query layer for analyst notebooks. Emphasize how these paths can coexist on a shared data platform without compromising performance or compliance.
Pro tip: Highlight the importance of data contracts and SLAs for each consumer to manage expectations and ensure reliability, and mention how you would monitor and evolve these paths as needs change.
For each consumer, identify key needs: latency (real-time vs. batch), data freshness, query patterns (aggregations vs. raw scans), concurrency, and governance (audit, lineage, access control).
Propose a pre-aggregated, denormalized layer (e.g., star schema or materialized views) optimized for low-latency, high-concurrency queries, with caching and a BI tool-friendly interface.
Recommend immutable, versioned snapshots with full audit trails, strict access controls, and scheduled batch exports to ensure reproducibility and compliance.
Suggest a flexible, SQL-based interface over raw or lightly processed data (e.g., data lake or warehouse) with on-demand compute, allowing exploration without impacting other consumers.
Explain how these paths share a common data foundation (e.g., lakehouse architecture) and how you enforce consistent security, lineage, and monitoring across all consumers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.