Technical Skills and Tools Questions

A concise but comprehensive presentation of a candidate's core technical competencies, tool familiarity, and practical proficiency. Topics to cover include programming languages and skill levels, frameworks and libraries, development tools and debuggers, relational and non relational databases, cloud platforms, containerization and orchestration, continuous integration and continuous deployment practices, business intelligence and analytics tools, data analysis libraries and machine learning toolkits, embedded systems and microcontroller experience, and any domain specific tooling. Candidates should communicate both breadth and depth: identify primary strengths, describe representative tasks they can perform independently, and call out areas of emerging competence. Provide brief concrete examples of projects or analyses where specific tools and technologies were applied and quantify outcomes or impact when possible, while avoiding long project storytelling. Prepare a two to three minute verbal summary that links skills and tools to concrete outcomes, and be ready for follow up probes about technical decisions, trade offs, and how tools were used to deliver results.

MediumTechnical

0 practiced

Compare Snowflake, BigQuery, and Redshift specifically for BI workloads. Evaluate on: concurrency for dashboards, cost model predictability, performance for ad-hoc queries, ease of administration, and integration with common BI tools. Give a recommendation based on a startup expecting fast growth and unpredictable query patterns.

HardTechnical

0 practiced

How would you integrate a simple ML scoring model (e.g., churn risk) into BI dashboards used by account managers? Cover model serving, feature computation (real-time vs batch), versioning, explainability, and how you'd present model confidence/benchmarks to users.

MediumTechnical

0 practiced

Propose a pragmatic approach to introduce data contracts between data producers (transactional systems) and the BI team. Include an example contract schema elements, enforcement mechanisms (tests, CI), and how you would handle backward-incompatible schema changes.

MediumSystem Design

0 practiced

Design a star-schema data model for an e-commerce analytics warehouse. Requirements: support orders, refunds, product catalog, promotions, customer lifecycle analysis, and 7+ years of historical data with daily ETL. Describe fact and dimension tables (names, grain), keys, partitioning strategy, and how you'd implement Slowly Changing Dimensions (type 2).

HardTechnical

0 practiced

High-cardinality dimensions (e.g., device_id, user_id) cause slow joins and heavy query costs for dashboard-level queries. Discuss techniques to handle such dimensions in the semantic layer and warehouse (pre-aggregation, approximate counting, sampling, bloom filters, dimensional bucketing). For each technique, explain trade-offs in accuracy, latency, and maintenance.

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