Databricks·Technical Product Manager·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

TPM interview at Databricks, just the one question from what I can tell. Short and technical, focused on observability for microservices.

Questions Asked (1)

How would you collect health and observability data across deployed microservices?

System DesignProduct Analytics & MetricsTechnical Trade-offs

Author's notes

I went straight to metrics and logging pipelines, talked about aggregating service health signals and setting up alerting thresholds.

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AI HintsAI Generated

Suggested Approach

Start by framing observability as a product capability that serves multiple personas (SREs, developers, PMs) and aligns with Databricks' data platform strengths. Then walk through a layered architecture: collection (agents, SDKs, sidecars), transport (Kafka, OTel Collector), storage (Delta Lake, time-series DBs), and consumption (dashboards, alerts, product analytics). Emphasize trade-offs between cost, latency, and granularity, and tie back to product metrics like adoption and reliability SLAs.

Pro tip: Anchor your answer in OpenTelemetry as the emerging standard and highlight how Databricks can unify observability data with business data in Delta Lake for cross-domain insights—this shows you think like a Databricks PM, not just a generic one.

1. Define goals and personas

Clarify what health and observability mean for different users: SREs need uptime and latency, developers need traces and logs, PMs need adoption and feature usage. Align metrics with business SLAs and product KPIs.

2. Choose collection methods

Select instrumentation approaches: agents (e.g., Datadog, OTel Collector), SDKs for custom metrics, sidecars for service mesh, and log shippers. Balance auto-instrumentation vs. manual for flexibility and overhead.

3. Design data pipeline and storage

Architect a scalable pipeline: use Kafka or OTel Collector for ingestion, then store in Delta Lake for unified analytics or specialized TSDBs for high-cardinality metrics. Consider retention, sampling, and cost.

4. Enable consumption and action

Provide dashboards (Grafana, Databricks SQL), alerting (PagerDuty), and self-serve analytics for PMs. Ensure data is queryable and joinable with business data for root-cause and impact analysis.

5. Iterate with trade-offs

Discuss trade-offs: cost vs. granularity, real-time vs. batch, vendor lock-in vs. open standards. Propose a phased rollout with feedback loops to refine coverage and value.

Key Points to Mention

OpenTelemetry for vendor-neutral instrumentation and traces, metrics, logs
Delta Lake for unified observability and business data analytics
Sampling and aggregation strategies to manage cost and cardinality
Service mesh (e.g., Istio) for automatic telemetry collection
SLIs/SLOs and error budgets to tie observability to reliability
Product analytics integration to measure feature adoption and user impact

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.