These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Approach preparation like you would a high‑stakes analytics engagement: clarify goals, define metrics, build a plan, then iterate. Focus first on the role’s operating context (support analytics at scale), then drill into SQL fluency, metrics/BI rigor, and your ability to convert ambiguous signals into decisions.

• Role-related knowledge (Support & Product Analytics) – Interviewers assess whether you can define service‑health metrics (e.g., SLAs, FCR, deflection), analyze ticket volumes, and connect product telemetry to user friction. Demonstrate fluency with support datasets, taxonomy design, and how LLM‑based classification changes the analysis surface.
• Analytical problem‑solving – You’ll be evaluated on how you structure ambiguous questions, choose methods appropriately, and pressure‑test conclusions. Expect case prompts that require clear assumptions, tidy SQL, and thoughtful interpretation rather than one “right” answer.
• Technical depth (SQL, Python, BI) – Expect expert‑level SQL, pragmatic Python/R for analysis/automation, and BI craft focused on clarity and self‑serve usability. Strong candidates write production‑ready SQL, propose data‑quality checks, and design dashboards that scale.
• Communication and influence – Interviewers look for crisp narratives that align cross‑functional teams. Use executive‑ready storytelling, provide trade‑offs, and surface decision‑grade recommendations—not just charts.
• Ownership and velocity – The bar emphasizes bias to action, zero‑defect execution, and rapid prototyping (e.g., notebooks, Retool). Show how you de‑risk ambiguity, validate quickly, and harden with Engineering.

Public reports about OpenAI analytics interviews consistently point to a rigorous, case‑driven process emphasizing practical analysis over theory. You should expect fast pacing, deep dives into SQL and metrics fluency, and sessions that mirror the role’s day‑to‑day: investigating friction signals, instrumenting service‑health views, and proposing operational recommendations. The tone is collaborative but exacting—interviewers probe assumptions, data quality, and stakeholder alignment.

The process typically progresses from a recruiter screen to manager/peer technical conversations, followed by a multi‑thread onsite or virtual panel. Themes include live SQL, case work in notebooks or whiteboards, dashboard reasoning, taxonomy/metric definitions, and cross‑functional communication. Expect to discuss LLM‑based classification, pipeline reliability, and how you’d scale self‑serve analytics.

Compared with many companies, OpenAI places stronger emphasis on real‑world decision‑making and speed to impact. You will be asked to prototype solutions conceptually, defend governance choices, and articulate how your work changes operational outcomes. Preparation should reflect this bias toward practical rigor and stakeholder clarity.

This visual outlines a typical flow: initial screen, hiring manager deep dive, technical SQL/analysis rounds, a cross‑functional onsite panel (BI, product/support case, communication), and a values/behavioral conversation. Use it to time‑box your prep, sequencing SQL drills first, then metrics/BI design, and finally product/support cases and storytelling. Stages can vary by team and seniority; your recruiter will confirm your exact path.

SQL and Analytical Foundations

Strong SQL underpins everything—especially with heterogeneous support and product data. Interviewers evaluate your ability to write performant, correct SQL on the first pass, reason about schemas quickly, and build analysis that stands up to scrutiny. Excellence means clean logic, correct window/aggregation use, and clear commentary about assumptions and data quality.

Be ready to go over:

• Time series and volumes – Weekly ticket volume, moving averages, trend breaks, and seasonality.

• SLAs and latency – Computing resolution time distributions, SLA attainment, and backlog views.

• Joins and data modeling – Joining tickets, messages, users, plans, and BPO partner data accurately.

• Advanced concepts (less common):

  • Late‑arriving data and backfills
  • Slowly changing dimensions (e.g., plan tier at time of ticket)
  • Performance tuning (CTEs vs. subqueries, partitioning, clustering)

Example questions or scenarios:

• “Given tickets(id, created_at, resolved_at, channel, bpo_partner_id, sla_minutes) and messages(id, ticket_id, sender, created_at), write SQL to compute weekly SLA attainment by channel and partner, including 7‑day rolling averages.”
• “Identify users with ≥3 tickets within 30 days of plan upgrade. Return counts and first‑response latency quantiles.”
• “Find top 10 emerging topics by week using a tags table and detect significant week‑over‑week spikes.”

Metrics, Taxonomy, and BI Craft

This role defines and scales the metrics taxonomy used by Operations and partners. Interviews test whether you can formalize KPIs, document definitions, and build self‑serve dashboards that enable non‑technical users to answer questions unassisted. Strong performance shows clear metric specs, thoughtful drill paths, and an insistence on a single source of truth.

Be ready to go over:

• Service‑health KPIs – FCR, deflection, SLA attainment, backlog, ticket mix, and CSAT.

• Metric governance – Naming, ownership, refresh cadences, and data‑quality checks.

• Dashboard design – Layout for scanning, filters, role‑based views, and alerting thresholds.

• Advanced concepts (less common):

  • Partner scorecards and automated data sharing with BPOs
  • Defining “resolved” consistently across channels and products
  • Leading vs. lagging indicators of friction

Example questions or scenarios:

• “Propose a top‑to‑bottom dashboard for service health that an executive and a frontline manager can both use. What are the tiers and drill‑downs?”
• “Define deflection robustly across docs, in‑product help, and bot handoffs. How would you validate the metric?”
• “A team disputes FCR. How do you redefine and implement it to prevent gaming?”

LLM‑Powered Text Classification and NLP

You will be expected to leverage LLMs to classify inbound volumes, surface sentiment, and accelerate root‑cause analysis. Interviewers probe your practical approach: data prep, labeling strategies, prompt or fine‑tuning choices, evaluation, and deployment patterns. Strong candidates reason about precision/recall trade‑offs, feedback loops, and operationalization within pipelines/BI.

Be ready to go over:

• Taxonomy design for text – Topic granularity, hierarchical tags, and evolution over time.

• Prompting vs. fine‑tuning – Cost, latency, control, and data privacy considerations.

• Evaluation – Test sets, confusion analysis, drift monitoring, and human‑in‑the‑loop QA.

• Advanced concepts (less common):

  • Few‑shot prompting with constraints and chain‑of‑thought safeguards
  • Embedding‑based retrieval to enrich classification context
  • Real‑time routing to specialized queues based on LLM outputs

Example questions or scenarios:

• “Design an LLM‑based pipeline to auto‑tag tickets into 20 topics and 5 severities. How do you evaluate and calibrate thresholds?”
• “Your classifier drifts after a product launch. What telemetry and guardrails do you add?”
• “Walk through a prompt strategy that balances cost and accuracy for high‑volume, short messages.”

Data Engineering Interfaces and Reliability

You will partner closely with Data Engineering to ensure reliable pipelines, freshness, and quality. Interviews evaluate whether you anticipate failure modes, specify sources of truth, and encode checks that keep dashboards trustworthy. Strong candidates propose pragmatic instrumentation, SLAs for data, and rollback/alerting strategies.

Be ready to go over:

• Pipelines and freshness – Latency, backfills, scheduling, and dependencies.

• Data quality checks – Row‑count deltas, null spikes, schema changes, and reconciliation.

• Source of truth – Contracting with upstreams; versioning metric definitions.

• Advanced concepts (less common):

  • Idempotent backfills and late data handling
  • Multi‑region considerations for global support
  • Privacy/SOC2 implications for ticket content

Example questions or scenarios:

• “A core ticket table dropped 15% week‑over‑week. Diagnose, quarantine, and backfill—with minimal dashboard disruption.”
• “Propose a data contract for a new support channel, including SLAs and validation rules.”
• “How would you implement anomaly alerts for SLA attainment with seasonality?”

Communication, Stakeholder Management, and Decision‑Grade Storytelling

Your insights must change decisions. Interviewers test how you tailor narratives to executives vs. operations, pre‑empt objections, and drive alignment under ambiguity. Strong performance includes concise memos, structured trade‑offs, and next‑step recommendations tied to ownership and timelines.

Be ready to go over:

• Executive storytelling – Context, signal, implication, decision, and owner.

• Trade‑offs – Accuracy vs. speed, automation vs. manual QC, technical debt vs. velocity.

• Change management – Rolling out new metrics or dashboards with training and adoption plans.

• Advanced concepts (less common):

  • Communicating uncertainty and risk explicitly
  • Designing partner scorecards that incentivize desired behavior
  • Building self‑serve that actually reduces ad‑hoc requests

Example questions or scenarios:

• “You find a 20% spike in billing‑related tickets post‑launch. How do you brief leadership within 24 hours, and what actions do you recommend?”
• “Two orgs use different SLA definitions. How do you reconcile and drive adoption of a unified metric?”
• “An executive wants a metric that you believe is misleading. How do you respond?”

This visualization highlights the most frequent interview themes (e.g., SQL, SLAs, FCR, deflection, LLM classification, dashboards, data quality, storytelling). Larger terms appear more often in reported or representative prompts; use them to prioritize your preparation sprints. Aim to master the big clusters first (SQL/metrics/BI), then differentiate with LLM/NLP and governance depth.

Day to day, you will convert noisy support and product data into trusted, actionable systems. You will explore inbound volumes to surface friction signals, define and document a unified metrics taxonomy, and build dashboards that keep Sales, Technical Success, Product, Engineering, and BPO partners aligned. Your analyses will inform operational staffing, product prioritization, and self‑serve investments that drive deflection and user satisfaction.

You will prototype quickly in ChatGPT, notebooks, and Retool to validate ideas before hardening with Engineering. You will partner with Data Engineering on pipeline contracts and quality checks, and with Data Science on predictive models and experiments that forecast demand or quantify deflection impact. Expect to lead special deep dives for leadership during incidents or launches where speed and clarity matter.

Typical initiatives include:

• Designing an LLM‑based ticket classification system and integrating outputs into BI.
• Building a real‑time service‑health view with alerting for SLA risk and backlog spikes.
• Creating BPO scorecards and automated data sharing to standardize performance management.
• Leading post‑launch analyses that connect ticket trends to product changes and proposed fixes.

A competitive Data Analyst for User Operations blends advanced analytics with operational pragmatism. You bring expert SQL, practical Python/R, and BI design that drives self‑serve adoption. You are fluent in support metrics, data governance, and how LLMs reshape text analytics.

• Must‑have skills:

  • Expert‑level SQL; strong data modeling and window functions
  • Proficiency in Python or R for analysis and automation
  • Hands‑on BI experience (e.g., Looker, Mode, Tableau, Sundial) with self‑serve focus
  • Fluency with support metrics (SLAs, FCR, deflection) and service‑health KPIs
  • Experience with LLM prompting/fine‑tuning for classification/sentiment/tagging
  • Clear, executive‑ready storytelling; strong cross‑functional collaboration
  • Bias to action, quality rigor, and comfort with ambiguity

• Nice‑to‑have skills:

  • Experimentation/causal inference basics for deflection and product changes
  • Building partner scorecards, especially for BPOs
  • Advanced visualization (custom components, thoughtful alert design)
  • Data contracts, governance frameworks, and incident playbooks

• Experience level: Typically 8+ years in analytics/BI/data science, ideally within support or operations settings and fast‑moving product orgs. Backgrounds spanning analytics engineering or GTM analytics are a plus when paired with support domain fluency.