Why pre-screen business intelligence developers before the technical interview
The default outcome in this discipline is a dashboard that gets opened twice. The developers who avoid it start from the decision somebody needs to make, build the smallest thing that supports it, and can defend the number when a director says it looks wrong. A short screen asks what happens when the data contradicts what a stakeholder expected, which is the moment the job is decided.
What actually matters when screening Business Intelligence Developer candidates
- 01
Technical proficiency
Test SQL depth beyond joins: window functions, CTEs, query plans, incremental models in dbt, plus DAX or MDX measures and semantic layer design in Power BI, Tableau or Looker.
- 02
Systems and trade-offs
Probe how they chose between wide denormalised tables, aggregate extracts and live connections, and what they did about slow dashboards, row-level security and cost per query.
- 03
Evidence and rigour
Assess how they validate numbers: reconciliation against source systems, unit tests on models, data quality checks, and what happened when finance disputed a reported figure.
- 04
Collaboration and communication
Look for evidence of requirements gathering with non-technical users: metric definition workshops, dashboard adoption rates, documentation in a data catalogue, and training or handover to analysts.
Pre-screening questions to ask Business Intelligence Developer candidates
12 questions grouped by what they test. Ask the same set in every screen and score answers on a consistent scale, or send them as an async video screen and compare answers side by side.
Reports people use
3 questions01Can you give an example of using data to influence a business decision?
Listen forA decision that changed, with the analysis and the person who acted on it described.
Reports delivered with no decision attached, or influence claimed without a specific outcome.
02Have you been involved in the full lifecycle of a reporting project?
Listen forRequirements through to adoption, with usage checked after delivery rather than assumed.
Involvement limited to building visuals, or projects considered complete at handover.
03Do you have experience creating dashboards, and can you give examples?
Listen forDashboards designed around a decision, with unnecessary charts removed rather than added.
Dashboards packed with every available metric, or design driven by what is easy to plot.
Data work is solid
4 questions04How comfortable are you with querying data directly for analysis?
Listen forConfident query writing including joins and window functions, with query performance considered too.
Queries built entirely through a visual interface, or slow queries escalated immediately.
05Do you have experience working with data warehouses?
Listen forWarehouse structure understood, with the difference between raw and modelled layers respected.
Reports built directly on production systems, or transformations duplicated in every dashboard.
06What is your experience with data modelling?
Listen forModels designed for the questions being asked, with grain and relationships defined deliberately.
Everything flattened into one wide table, or model grain confused between fact tables.
07Can you describe integrating data from several different sources?
Listen forKeys reconciled across systems, with mismatches investigated rather than resolved by exclusion.
Records dropped when keys do not match, or discrepancies between sources left unexplained.
Numbers defensible
3 questions08Can you describe dealing with missing or badly formatted data?
Listen forData quality issues surfaced to owners and documented, not silently patched in a report.
Gaps filled with assumptions, or quality problems hidden inside transformation logic.
09Do you have experience conducting root cause analysis?
Listen forA metric movement traced to a cause, distinguishing a real change from a data pipeline problem.
Movements explained by assumption, or pipeline failures reported as business changes.
10Can you give examples of performance indicators you have developed?
Listen forDefinitions agreed with the business and documented, so the same number means one thing.
Metrics defined differently in different reports, or definitions never written down.
Requirements from users
2 questions11How would you handle data that contradicts what stakeholders expected?
Listen forWork checked first, then the number defended with evidence and the discrepancy explained.
Reports adjusted to match expectations, or awkward results left uncirculated without explanation.
12How would you handle an urgent request for a report at short notice?
Listen forThe actual question clarified quickly, with caveats stated on anything produced under time pressure.
Numbers produced without caveats, or urgent requests delivered without checking the question.
How to score responses
Score every candidate on the same four criteria immediately after the screen. At this stage you are shortlisting for panel interviews, not making the final call.
Technical proficiency
35%5Writes tuned SQL against warehouses like Snowflake or BigQuery, explains star schema grain choices, and debugs DAX filter context fluently.
Systems and trade-offs
25%5Names a concrete trade-off, for example moving from direct query to imported aggregates, with the refresh and cost consequences they accepted.
Evidence and rigour
25%5Describes reconciliation routines and tests that caught discrepancies before stakeholders did, with named checks and the defect they prevented.
Collaboration and communication
15%5Shows dashboards people actually use, agreed metric definitions written down, and a habit of pushing back on vague reporting requests.
The default outcome is a dashboard opened twice. A one-way video screen asks which of theirs people actually use.
Try it on HirevireScreening FAQ
Process basics
How long should a pre-screening round for this role take?
Fifteen minutes across eight to ten questions, answered async. Enough to establish reports people use, test their data engineering depth, and hear how they handle stakeholder pushback.
How much engineering should I expect?
Enough to model data and write queries that perform, not only build visuals. A developer who depends on somebody else for every dataset will be blocked most weeks.
Evaluating answers
What is the strongest signal when screening this role?
What they do when data contradicts an expectation. Strong developers check their work then hold the number with evidence. Anyone who adjusts the report to match will hide real problems.
How do I judge whether their work gets used?
Ask which of their dashboards is opened most and why. Real answers include usage figures and a decision it supports. Anyone who does not know has built reporting nobody needed.
Go deeper on this role
Sanat has been hiring since 2012 and watching the recruitment industry change up close ever since, and turned that screening process into Hirevire's video screening platform. LinkedIn
