Role Team and Infrastructure Questions Questions

Guides asking targeted questions about the specific role, team responsibilities, and the technical or operational infrastructure that supports the role. Topics include typical responsibilities, on call rotations or support models, current infrastructure challenges, tech stack or tooling, success metrics for the role, collaboration with adjacent teams, opportunities for growth, and infrastructure priorities. This helps candidates demonstrate role understanding and probe for operational and strategic expectations.

EasyTechnical

52 practiced

What internal metrics does the BI team track to measure its impact and operational health? For example: dashboard adoption, number of active reports, SLA compliance for data freshness, mean time to detect/fix incidents, stakeholder satisfaction scores, and time-to-insight for ad-hoc requests.

MediumSystem Design

49 practiced

Walk me through the typical process and timeline for integrating a new data source into the warehouse and reporting layer: discovery, schema mapping, ETL development, testing and reconciliation, staging validation, and production deployment. Who signs off at each stage?

Sample Answer

Requirements & discovery (1–2 weeks)- Activities: meet business owner(s) to capture use cases, KPIs, data frequency, SLAs, and privacy/GDPR concerns; inventory source system (tables, API, file formats), data owners, and access.- Sign-off: Business stakeholder + Data Owner (confirm requirements, access).

Schema mapping & design (3–5 days)- Activities: map source fields to warehouse model (star/dim-fact), define transformations, surrogate keys, data types, partitioning, and retention. Document column-level definitions and lineage.- Sign-off: BI Lead + Data Engineer + Business Owner (approve mapping and definitions).

ETL/ELT development (1–2 weeks)- Activities: build pipeline (incremental logic, CDC or batch), implement transformations, error handling, logging, and alerting. Add metadata and lineage tracking.- Sign-off: Data Engineer (dev complete) + BI Analyst (preliminary validation).

Testing & reconciliation (1 week)- Activities: unit tests, integration tests, test with representative data volumes; reconciliation scripts comparing source vs warehouse (row counts, aggregates, key metrics); data quality rules.- Sign-off: QA/Data QA Lead + Data Owner (reconciliation OK) + BI Analyst (metrics match expectations).

Staging validation & UAT (3–5 days)- Activities: deploy to staging, run full refresh, have business users validate reports/dashboards, performance checks, and regression tests.- Sign-off: Business Owner + BI Stakeholders (UAT sign-off).

Production deployment & monitoring (1–2 days + ongoing)- Activities: schedule cutover, enable production jobs, run smoke checks, set up dashboards/alerts for freshness and error rates, document runbooks and rollback plan.- Sign-off: Data Platform Owner/Release Manager + Business Owner (go-live approval).

Post-deploy (2–4 weeks)- Activities: monitor, capture feedback, tune performance, and finalize documentation. Close loop with business on any adjustments.- Sign-off: Business Owner confirms acceptance and KPI stability.

Notes on timelines: vary by complexity (API vs manual files, CDC vs full refresh). Key principles: clear ownership at each stage, automated reconciliation, explicit SLAs for data freshness, and documented lineage so BI consumers trust the data.

HardSystem Design

82 practiced

Outline a disaster recovery (DR) plan for the BI platform that ensures critical dashboards remain available and data integrity is preserved during a major cloud-region outage. Include RTO/RPO targets, failover strategies, replication, backup cadence, testing cadence, and runbooks for recovery.

Sample Answer

Requirements & targets:- Critical executive dashboards: RTO = 30 minutes, RPO = 15 minutes.- Operational/near-real-time dashboards: RTO = 2 hours, RPO = 1 hour.- Historical/archival reports: RTO = 4+ hours, RPO = 24 hours.

High-level strategy:- Multi-region architecture: primary region serves writes and BI compute; a warm standby region holds continuously-replicated data and BI artifacts to allow fast failover.- Separation of concerns: data ingestion/warehouse, reporting layer (Looker/Power BI/Tableau server), and object store for extracts/backups all replicated.

Replication & backups:- Database: continuous change data capture (CDC) streaming (e.g., Debezium / cloud DB replication) to a cross-region read-replica in near real-time (meets 15-min RPO).- Data warehouse snapshots: hourly incremental snapshots + daily full snapshot to object store (S3/GCS) in both regions.- BI artifacts: version-controlled dashboards and metadata exported nightly to repo and replicated to standby BI servers.- Transaction log shipping: keep last 72 hours of WAL/transaction logs for point-in-time recovery.- Backup cadence: hourly incremental, daily full, weekly archival retained 90 days (or per policy).

Failover strategies:- Automated failover for read-only dashboards via DNS + LB health checks: low-latency redirect to read-replica for non-authoring users.- Warm failover for full service: promote standby DB replica to primary (manual/automated with guardrails), start BI server instances from replicated images, restore DNS with low TTL, enable feature flags to route write-heavy jobs to degraded mode.- Graceful degradation: queued ingestion and report refresh throttling; present cached snapshots for critical dashboards if live compute unavailable.

Testing cadence:- Monthly smoke tests: verify replica freshness, dashboard rendering, and scheduled extracts on standby.- Quarterly full failover drill: promote standby, run smoke & regression tests, switch routing, measure RTO/RPO, record runbook times.- Annual business continuity exercise with stakeholders and simulated data-loss scenarios.

Runbook (abridged for critical dashboard failover):1. Detection: alert from region health monitors → declare Region-Outage incident.2. Triage: confirm scope (network vs. service vs. DB). Notify on-call BI lead and SRE.3. Immediate mitigation (0–15 min): switch LB to cached-dashboard mode; notify users with status page.4. Promote replica (15–30 min): validate replica lag < RPO, run pre-promotion checks, promote read-replica to primary (DB team).5. Spin up BI servers (parallel): deploy latest artifact snapshot to standby BI cluster, attach to promoted DB.6. DNS & routing (20–30 min): update DNS/LB with low TTL, verify sessions, disable write-heavy jobs.7. Validation (30–45 min): run smoke queries for top 10 critical dashboards, check data integrity (row counts, checksums), confirm scheduled reports.8. Post-failover: run integrity audit using transaction logs, reconcile missing events via CDC replay if needed.9. Recovery to primary: after primary region healthy, plan controlled failback with DB sync, validate, and failback during maintenance window.10. Communication: send incident timeline, user impact, and remediation steps to stakeholders.

Key operational items:- Maintain runbooks in repo, keep dashboard owners list and SLA table updated.- Automate health checks for top-N dashboards and dashboard-level synthetic queries.- Maintain IAM keys/service accounts in both regions; rotate keys in CI.- Postmortem after every drill or real outage; update RTO/RPO and runbooks accordingly.

This plan balances availability and data integrity while enabling BI analysts to continue delivering critical insights with clear, testable procedures.

EasyTechnical

41 practiced

Does the BI team have an on-call or support rotation for production dashboards, data pipelines, or reporting platforms? If yes, describe the rotation length, expected response SLAs, escalation path, tools used for paging and incident tracking (PagerDuty, Opsgenie, Slack), and how handoffs are performed between shifts.

MediumTechnical

42 practiced

How does the team handle PII and regulatory constraints (GDPR, CCPA, HIPAA) in reports and dashboards? Ask about anonymization/masking, role-based access, audit logs, data retention policies, and processes for granting temporary data access for analyses.

Sample Answer

I’d describe a pragmatic, compliance-first approach I use as a BI analyst that balances privacy with analytical needs.

Anonymization / Masking- Prefer pseudonymization (replace identifiers with consistent tokens) for analytics; apply stronger anonymization (k‑anonymity, aggregation, noise) before publishing dashboards with wider audiences.- Implement field-level masking in the BI layer (LookML/Looker, Power BI row-level expressions) and use database features (Dynamic Data Masking in SQL Server, masked columns in Snowflake) for sensitive columns.

Role-based access- Enforce least-privilege via RBAC and row-level security: developers get broader dataset access in dev; business users only see aggregated or their own-entity rows in prod.- Map BI tool groups to identity provider groups (Okta / Azure AD) for central management.

Audit logs & monitoring- Enable query and access logging at DB (Snowflake/Athena/Redshift) and BI layers; ship logs to SIEM for anomaly detection.- Retain detailed logs for investigation windows required by GDPR/CCPA/HIPAA and surface changes to sensitive reports.

Data retention & minimization- Apply retention windows in ETL (drop PII after business need ends); archive or purge per policy. Use data minimization — only ingest fields required for the report.

Temporary access for analyses- Use a formal request workflow (ticketing + data owner and DPO approval), grant time‑boxed credentials or temporary elevated roles, and require justification and logging. Revoke automatically after expiration and review outstanding grants periodically.

Additional controls- Encryption at rest/in transit, DPIAs for new data products, and automated tests to detect PII in published dashboards (sensitive-pattern detectors) round out the process.

Example: for a churn model I’d pseudonymize customer_id in the warehouse, expose aggregated cohorts in Power BI via RLS, and if a data scientist needs raw PII for troubleshooting they open a JIRA request, get DPO approval, receive a 72‑hour scoped role, and all queries are logged for audit.

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