Analytics Fundamentals: SQL, Python, and Statistics
Strong fundamentals are non-negotiable. We assess your ability to write efficient SQL, manipulate datasets in Python/PySpark, and apply basic statistics to validate findings. Expect to optimize queries, reason about performance, and implement pragmatic checks that build trust in results.
Be ready to go over:
- SQL (joins, windows, CTEs, performance): Write and optimize queries, reason about partitions, indexes, and scalability on big data systems.
- Python/PySpark for data wrangling: Transformations, aggregations, UDFs vs. built-ins, handling skew, and memory trade-offs.
- Applied statistics: Sampling, confidence intervals, experiment basics, and error sources; when “directionally correct” is good enough.
- Advanced concepts (less common): Query tuning on Lakehouse tables, Delta Lake optimization, cost-aware compute choices, time series decomposition.
Example questions or scenarios:
- "Given fact_sales and dim_calendar, compute 28‑day rolling GM% by region and product, handling late-arriving facts."
- "A PySpark job is skewing on a single key; how do you diagnose and fix it?"
- "Your dashboard shows a sudden KPI shift; what statistical checks do you run before escalation?"
Data Modeling, Pipelines, and Governance
NVIDIA teams rely on durable data products. You’ll discuss star schemas, semantic layers, and how you engineer pipelines that are observable, documented, and trusted. Governance is part of the job: business glossaries, metric definitions, lineage, and data quality SLAs.
Be ready to go over:
- Dimensional modeling and semantic layers: Facts/dims, slowly changing dimensions, curated datasets for BI/AI apps.
- Pipelines on Databricks/Snowflake: Ingestion patterns, orchestration, Delta Lake, and cost/performance optimization.
- Data quality and observability: Automated checks, auditing, alerting, backfills, and change control.
- Advanced concepts (less common): Cross-plant schema standardization, CDC from SAP, metadata to reduce AI hallucinations.
Example questions or scenarios:
- "Design a semantic layer for spend tracking that standardizes ‘supplier’ and ‘cost center’ across regions."
- "Propose DQ checks for a finance P&L dataset with late adjustments."
- "How would you reduce BI query friction for 500+ users on Lakehouse?"
Business and Domain Analytics: Finance, Operations, and Datacenter
Your impact is measured in decisions improved. We assess your ability to model TCO for GPU workloads, interpret financial metrics, and streamline operations. You’ll be asked to translate business questions into analytical frameworks and quantify trade-offs.
Be ready to go over:
- Finance analytics (FP&A, variance, working capital): Source from SAP, reconcile logic, build trustworthy metrics.
- Operations (manufacturing integrations, supply chain KPIs): Cross-site data alignment, lead-time, yield, quality metrics.
- Datacenter/GPU economics: FLOPS, bandwidths, model throughput, utilization, and cost drivers for training/inference.
- Advanced concepts (less common): Token economics for LLMs, capacity planning, scenario modeling under constraints.
Example questions or scenarios:
- "Build a simple model estimating LLM inference TCO across two GPU generations; what assumptions matter most?"
- "Your spend dashboard undercounts capitalized costs; how do you root-cause using SAP/BW data?"
- "Which metrics would you track to evaluate a new manufacturing plant integration?"
Visualization, Metrics, and Storytelling
Great analysis must be consumable. We look for Power BI/Tableau proficiency, metric design, and crisp narratives. You should move from a wall of data to three insights and a recommendation, backed by reproducible logic.
Be ready to go over:
- Dashboard design: Layout, filters, semantic consistency, performance tuning, and documentation of metric logic.
- Metric stewardship: Definitions, ownership, versioning, and governance to prevent drift.
- Executive communication: Writing succinct readouts and presenting trade-offs to mixed audiences.
- Advanced concepts (less common): Enabling self-service at scale, certified datasets, usage analytics for adoption.
Example questions or scenarios:
- "Redesign a cluttered KPI dashboard: what do you remove, what do you annotate, and why?"
- "Explain variance drivers to a non-technical stakeholder in three slides."
- "How do you document metrics to avoid conflicting definitions across teams?"
Influence, Ownership, and Delivery
You succeed by aligning stakeholders and shipping. We evaluate how you drive consensus, enforce standards, and make good calls under uncertainty. Bring concrete examples of projects where you moved the needle.
Be ready to go over:
- Stakeholder alignment: Framing decisions, clarifying requirements, and negotiating trade-offs.
- Execution at pace: Scoping MVPs, iterating quickly, instituting change control and release notes.
- Raising the bar: Introducing governance, standard schemas, and adoption strategies.
- Advanced concepts (less common): Leading multi-site data integrations, mentoring analysts, influencing architecture.
Example questions or scenarios:
- "Describe a time you enforced a metric standard against pushback—how did you win adoption?"
- "You have two weeks to deliver a P1 dashboard; what do you ship first and why?"
- "A stakeholder requests a metric that conflicts with global definitions—what’s your approach?"
