Work Style and Culture Fit Questions

Situation: The company prioritizes "move fast and iterate" but leadership still needs reliable dashboards for decisions.

Approach (high-level): I balance speed and safety by delivering small, observable increments with automated validation and lightweight governance so stakeholders get value quickly without exposing them to bad data.

Process changes I’d implement:

• Incremental delivery: break dashboards into MVP slices (key metric tiles first), ship iteratively and gather feedback.
• Instrumentation & lineage: enforce event/schema contracts; add clear provenance for each metric (SQL, source tables, transform version) visible in the dashboard.
• Automated validation: add unit/integration tests (dbt tests, SQL assertions) for transformations; include sanity checks (e.g., null rates, monotonicity) and compare to prior-day baselines.
• Deployment safety: use “shadow” or canary dashboards and feature-flagged metrics so new logic runs in parallel to existing metrics for 24–72 hours before becoming primary.
• Monitoring & alerts: set up data-quality alerts and an errors dashboard (failing tests, freshness lag, large deltas) routed to on-call BI or Slack channel.
• Lightweight governance: require data contracts and owner attribution for critical metrics, but keep approval fast (e.g., 24-hour SLAs). Maintain a metrics catalog (with owners, definitions, last-tested).
• Stakeholder communication: annotate dashboards with confidence level and last-validated timestamp; surface breaking changes in advance.

Example: When redesigning revenue metrics, I implemented dbt tests for revenue joins, deployed the new calculation behind a feature flag, ran it in shadow mode for 48 hours comparing outputs, triggered alerts when daily delta exceeded 2%, then flipped the flag after sign-off.

Why it works: Small iterative releases reduce blast radius; automated tests and shadowing catch regressions early; explicit lineage and owners make issues fast to resolve — all aligned with moving fast while protecting decision integrity.

Situation: At my previous company, the Site Reliability Engineering (SRE) team asked for a dashboard to track service health. Their team values were reliability and ownership — they wanted something that helped them proactively spot issues and clearly assign accountability.

Task: I needed to build a dashboard that aligned with those values: emphasize uptime and error trends, make incident ownership explicit, and enable fast investigation without adding noise.

Action:

• Met with the SRE lead and two on-call engineers to clarify definitions (what counts as an incident, SLAs, ownership rules).
• Mapped data sources (Prometheus alerts, incident tracker, deployment logs) and defined KPIs: uptime %, MTTR, incident count by service, recent deploys correlated with incidents.
• Designed the dashboard in Looker with clear sections: high-level reliability summary, trending charts (7/30-day MTTR & error rate), and an incidents table with owner, status, and direct link to the ticket.
• Added conditional coloring and a “needs attention” flag when MTTR or error rate exceeded thresholds; included a single-click filter to view only services owned by a specific engineer.
• Iterated twice based on on-call feedback to reduce clutter and surface root-cause links.

Result: Dashboard adoption went from zero to 100% of on-call rotations within two weeks. MTTR decreased by 18% over the next quarter (tracked in the dashboard), and SRE feedback highlighted that ownership was clearer and handoffs faster. The SRE lead called the dashboard “instrumental” in reducing noisy alerts and improving accountability.