Excel Impact·Technical Product Manager·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Interviewed for a TPM role at Excel Impact. One technical question about data warehousing came up and it was more strategic than I expected for this kind of screen.

Questions Asked (1)

How would you tackle the problem of unstructured data sitting in a data warehouse when clients are complaining about slow or inefficient reports?

Data ModelingTechnical Trade-offsProduct Analytics & Metrics

Author's notes

I went straight to schema-on-read vs schema-on-write and probably lost them a bit.

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AI HintsAI Generated

Suggested Approach

Start by diagnosing the root causes of slow reports through profiling and stakeholder interviews, then propose a layered solution that combines data modeling improvements (e.g., star schema, partitioning) with product-level trade-offs (e.g., pre-aggregation, caching). Frame your answer around measurable outcomes like query latency reduction and client satisfaction, and emphasize iterative delivery with clear success metrics.

Pro tip: Quantify the impact early—e.g., 'A 30% reduction in report load time could increase client retention by X%'—to show you think like a product manager, not just an engineer. Also, mention that you'd validate assumptions with a quick proof-of-concept before committing to a full migration.

1. Diagnose and Prioritize

Profile slow queries, identify which reports and clients are most affected, and quantify the business impact (e.g., time lost, churn risk). Interview stakeholders to understand their pain points and define success metrics.

2. Assess Data and Architecture

Analyze the unstructured data's format, volume, and query patterns. Evaluate current warehouse capabilities (e.g., indexing, partitioning) and identify bottlenecks such as full-table scans or lack of proper schema.

3. Propose Solutions with Trade-offs

Outline options like schema-on-read, ETL to structured models, pre-aggregation, or caching. For each, discuss trade-offs in cost, latency, flexibility, and maintenance, and recommend a phased approach.

4. Validate with a Pilot

Run a proof-of-concept on a subset of data or a single client to measure performance improvements and gather feedback. Use metrics like query time reduction and user satisfaction to justify broader rollout.

5. Implement and Iterate

Roll out the solution incrementally, monitor key metrics, and establish a feedback loop for continuous improvement. Document learnings and adjust the roadmap based on client needs and technical constraints.

Key Points to Mention

Root cause analysis: distinguish between data volume, query complexity, and infrastructure limitations.
Data modeling techniques: star schema, snowflake schema, partitioning, and indexing for unstructured data.
Trade-offs: cost vs. performance, flexibility vs. speed, and build vs. buy for tools like materialized views or caching layers.
Product metrics: define SLAs for report latency, track client satisfaction (NPS/CSAT), and measure adoption of improved reports.
Stakeholder communication: align engineering, data science, and client-facing teams on priorities and expectations.
Iterative approach: start with quick wins (e.g., query optimization) before larger architectural changes.

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