Ability to produce clear, concise, and well structured written artifacts including technical documentation, reports, and professional emails. Key skills include organizing content for different audiences, explaining methodology and assumptions, documenting metrics and data quality considerations, justifying visualizations, writing actionable summaries and next steps, crafting scannable and purpose driven emails with strong subject lines and calls to action, and maintaining an appropriate professional tone. Assessment looks for audience awareness, completeness without verbosity, and ability to produce handoff ready written work.
HardTechnical
61 practiced
You are the BI lead and must persuade senior leadership to fund a centralized data dictionary/documentation platform (e.g., DataHub, Collibra, Confluence + plugins). Draft a one-page memo (approx. 250–350 words) that outlines the current problem, expected benefits (quantified where possible), estimated costs, high-level implementation plan, and an adoption/rollout strategy. Address typical leadership concerns: ROI, maintenance burden, and speed to value.
Sample Answer
Subject: Proposal to Fund a Centralized Data Dictionary & Documentation PlatformExecutive summary — Our BI team spends ~20% of development time resolving ambiguous metrics, chasing lineage, and re-creating existing definitions. This causes slower dashboard delivery, inconsistent KPIs, and business trust issues. I recommend funding a centralized data dictionary/documentation platform (e.g., DataHub or Collibra, or Confluence + governance plugins) to standardize definitions, capture lineage, and enable self-service.Current problem- Ambiguity: Multiple conflicting definitions for metrics (e.g., “Active User”) across 6 teams.- Waste: Estimated ~1,100 developer-hours/year lost to triage (~$150k burden at fully loaded cost).- Risk: Inaccurate reports driving executive decisions and compliance exposure.Expected benefits (quantified)- Faster delivery: Reduce discovery/triage time by 50% → reclaim ~550 hours/year.- Consistency & trust: Reduce metric discrepancies by 80%, improving decision confidence and reducing rework.- Efficiency: Faster onboarding for analysts — cut ramp time from 6 to 3 weeks.- Risk reduction: Clear lineage reduces audit effort by estimated 40%.Estimated costs- Commercial tool (DataHub/Collibra): $80k–$200k/year licensing + one-time implementation ~$40k.- Confluence + plugins: $25k–$60k/year + $25k implementation.- Ongoing maintenance: ~0.2 FTE (gov’t owner) ≈ $25k/year.High-level implementation (6 months)1. Month 0–1: Select vendor via light POC with sample datasets.2. Month 2–3: Ingest critical data sources (warehouse, BI layer) and define 20 core business metrics.3. Month 4: Integrate lineage and access controls; create templates.4. Month 5–6: Train power users; pilot with 2 business units.5. Ongoing: Expand to org and automate syncs.Adoption/rollout strategy- Executive sponsorship + mandate for canonical definitions.- Start with quick wins (top 20 KPIs) to show value in 6–8 weeks.- Embed owners: require data stewards to maintain entries; include documentation in sprint acceptance criteria.- Measure success: time-to-resolution, metric discrepancy rate, and analyst ramp time.Addressing leadership concerns- ROI: Expected payback within 9–15 months through reclaimed analyst hours and reduced rework.- Maintenance: Minimal — 0.2 FTE plus automated syncs; stewardship tied to existing roles.- Speed to value: Pilot delivers measurable wins in 6–8 weeks; full ROI in first year.I request approval to begin vendor POC and budget allocation discussion.
MediumTechnical
68 practiced
Draft a cross-team handoff plan for delivering a new Marketing dashboard to the Marketing team. The plan should cover: training session outline, ownership transfer checklist, scheduled support window, SLA for bug fixes, and a three-step acceptance sign-off process. Keep the plan to about 8–12 bullet items with 1–2 sentence details each.
Sample Answer
- Project summary and goals: Deliver the new Marketing dashboard (Power BI/Looker) that tracks MQLs, campaign ROI, channel performance, and cohort retention; purpose, data sources, refresh cadence, and intended users will be summarized in a one-page handoff doc. - Training session outline (60–90 min): 15 min product walk-through, 20 min live demo of key flows (filters, drilldowns, exporting), 15 min hands-on exercises with sample tasks, 10 min Q&A, 15–30 min breakout for power-user scenarios and admin settings. - Training materials: Provide slide deck, quick-start cheat sheet, step-by-step admin runbook (data model, joins, calculated fields), and a short screen-recorded video for on-demand review. - Ownership transfer checklist: Confirm access to data sources, validate scheduled refreshes, transfer dashboard ownership in BI tool, share credentials for service accounts, list contact for ETL/warehouse owners, and confirm permissions for Marketing editors. - Scheduled support window: Engineering/BI team available for hypercare for two weeks post-handoff: Mon–Fri 9am–5pm with dedicated Slack channel and daily 30-minute standup for the first 5 days. - SLA for bug fixes: Critical (data accuracy/refresh fail) — response within 2 hours, target fix in 24 hours; High (broken visuals/filters) — response within 4 hours, fix in 3 business days; Medium/Low (UX polish, feature requests) — response within 2 business days, fix or backlog within two sprints. - Escalation path: First contact: BI analyst (name/email), second: BI manager, third: Data platform/ETL lead; include on-call rotation for critical incidents. - Acceptance criteria: Data correctness on sample KPIs, expected performance (page load <5s), and role-based access verified by Marketing product owner. - Three-step acceptance sign-off: 1) Marketing PO verifies functional criteria and signs “feature complete”; 2) Data owner validates data lineage and signs “data verified”; 3) BI manager approves production ownership and signs “production handoff complete.” - Post-handoff review: 30-day retrospective with Marketing to capture feedback, prioritize enhancements, and confirm SLA adherence; update documentation and schedule quarterly check-ins.
EasyTechnical
65 practiced
Write a reusable 3-line tooltip (title + two bullets, ≤40 words total) for a dashboard KPI card labeled 'Revenue Growth (YoY)'. The tooltip should help a non-technical executive interpret the number quickly and flag when additional analysis is recommended. Keep language neutral and avoid technical jargon.
Sample Answer
Revenue Growth (YoY)- Shows percent change in revenue versus same period last year.- If change is large or unexpected, review drivers: sales volume, price, channel.
EasyTechnical
67 practiced
Draft a concise, professional email to the Data Engineering team requesting missing customer-level mapping data needed for a weekly retention report. Include: subject, brief context (what report and why), tables/fields missing, an example row illustrating the issue, a hard deadline, and the action you will take if the data isn't available by the deadline.
Sample Answer
Subject: Urgent: Missing customer-level mapping for Weekly Retention Report — needed by Mon 24 Nov 2025 10:00 AM PTHi Data Engineering team,Context: Our Weekly Retention report (used by Growth and Product) fails to join customer activity to segmentation attributes. Without the customer-level mapping, retention metrics by cohort are incomplete and misreported.Missing table/fields:- dim_customer_mapping (expected source) - customer_id (PK) - segment_id - acquisition_channel - effective_date - statusExample problematic row (expected vs. found):- Expected: customer_id=12345 | segment_id=LOYAL | acquisition_channel=PaidSearch | effective_date=2025-11-20 | status=active- Actual: customer_id=12345 | segment_id=NULL | acquisition_channel=NULL | effective_date=NULL | status=NULLHard deadline: Please provide the complete/updated dim_customer_mapping table (or a patch with the missing fields populated) by 10:00 AM PT on Monday, 24 Nov 2025.If data is not available by the deadline, I will:1) Publish the report using last-known mappings and add a clear footnote about potential inaccuracies, AND2) Escalate to the Data Platform lead and notify report stakeholders of the data gap and expected timeline.If you need more context or a sample query to validate, I can share immediately. Thanks for the quick help — this drives key decisions for next-week campaigns.Best,[Your Name]Business Intelligence Analyst
MediumTechnical
51 practiced
Draft a README for an automated reporting pipeline built with dbt and Airflow that produces a daily 'Daily Sales' report. The README should include: purpose, high-level architecture diagram text, run schedule, preconditions, dependencies, a failure-handling checklist, how to rerun jobs manually, and primary contacts. Keep the README concise but complete enough for on-call engineers and analysts.
Sample Answer
PurposeThis pipeline produces the daily "Daily Sales" report (sales by SKU, region, channel; daily totals; returns) and refreshes downstream Looker/Power BI dashboards every morning so stakeholders have up-to-date revenue metrics.High-level architecture (text)Source DBs (OLTP, POS CSVs) → Airflow DAG (ingest & transform orchestration) → Raw tables in Analytics DB → dbt models (staging → marts → reports) → Analytics DB (final tables) → BI tool (scheduled extract/refresh)Run schedule- Airflow DAG: daily at 04:00 UTC- dbt run within DAG: runs after ingestion; finishes ~04:30 UTC- BI dashboard refresh: 05:00 UTCPreconditions- Source connections reachable (VPN, credentials)- Sufficient warehouse credits/quota- No upstream schema changes without notice- Latest POS CSVs delivered to S3 / network pathDependencies- Airflow (composer/managed) + DAG: dags/daily_sales.py- dbt project: /dbt/daily_sales, profiles.yml configured- Postgres/BigQuery/Snowflake analytics DB- S3 (or file share) for CSVs- Secrets manager for credentials- BI tool scheduled refreshFailure-handling checklist1. Check Airflow DAG logs for failed task and timestamp.2. If ingest failed: verify source availability, file presence, and network; re-run ingest task.3. If dbt failed: run `dbt test` locally or check `dbt run` error; fix failing model or seed; consider reverting recent schema changes.4. If warehouse resource errors: check quota/credits and resume or request more.5. If BI refresh failed: check DB table freshness and BI connector logs.6. Escalate if unresolved after steps below.How to rerun jobs manually- Airflow UI: Trigger DAG `daily_sales` for desired execution_date; for a single failed task: clear and re-run task.- CLI (if needed): - dbt: cd /dbt/daily_sales && dbt run --models report_daily_sales --profiles-dir ~/.dbt - Airflow CLI: airflow dags trigger daily_sales --conf '{"run_reason":"manual-rerun"}'- Validate: run dbt test and spot-check row counts / CI tests; confirm BI dashboard refresh at 05:00 or trigger manual refresh.Primary contacts- BI Owner: Alice Chen (alice.chen@company.com) — reports & metrics owner- Data Eng Lead: Marco Rivera (marco.rivera@company.com) — pipeline & Airflow- On-call rotation: data-platform-oncall@company.com (pager duty link)Notes- Include run_id and execution_date in any incident report.- If schema change is suspected, pause DAG and notify Data Eng before reruns.
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