1. What is a Data Analyst?

A Data Analyst at OpenAI translates raw, cross‑functional data into crisp narratives, decisions, and systems that scale. In User Operations, you turn support interactions, product usage, and operational telemetry into service‑health metrics, automated insights, and self‑serve tools that keep leaders and frontline teams aligned in real time. Your work reduces friction for customers, improves SLAs, and drives adoption of core products like ChatGPT and platform APIs.

This role sits at the intersection of Data Science, Engineering, and Operations, where the data surface is constantly evolving. You will define the metrics that matter (e.g., FCR, deflection, SLA attainment), instrument pipelines, build dashboards for Sales and Technical Success, and rapidly prototype LLM‑powered classifiers that transform unstructured text into actionable signals. The scale, data richness, and shipping velocity at OpenAI make this work both technically demanding and highly influential.

Expect to operate as both a builder and a partner. You will own end‑to‑end analytics—from schema literacy and SQL to BI and stakeholder storytelling—while collaborating on predictive models and experimentation. The payoff is direct: faster root cause analysis, proactive friction detection, and a unified view of service health that keeps OpenAI ahead of demand.

2. Getting Ready for Your Interviews

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.

3. Interview Process Overview

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.

4. Deep Dive into Evaluation Areas

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.

5. Key Responsibilities

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.

6. Role Requirements & Qualifications

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.

7. Common Interview Questions

These questions are representative of patterns seen in public candidate reports and align with this role’s responsibilities. Exact prompts vary by team; treat this as a practice map to sharpen fundamentals and case structure.

SQL and Data Modeling

Assesses your ability to manipulate realistic schemas and produce correct, performant queries.

• Write a query to compute weekly SLA attainment, the 90th percentile time‑to‑first‑response, and backlog by channel.
• Identify new vs. repeat users driving ticket volume; return top 5 growth cohorts with WoW deltas.
• Derive a ticket funnel (created → first response → resolved) with conversion rates and time‑to‑stage.
• Given a tags table, find the top emerging topics using a 4‑week rolling z‑score.
• Detect and exclude bot messages when computing FCR; document assumptions.

Metrics, BI, and Dashboard Design

Evaluates metric definitions, governance, and self‑serve clarity.

• Propose a unified definition for “deflection” across docs and bot flows; outline validation steps.
• Design an executive dashboard for service health; what KPIs and drill‑downs do you include?
• How would you reduce metric drift across teams and prevent “multiple sources of truth”?
• A team requests a new metric that conflicts with your taxonomy. How do you proceed?
• Present a plan to measure the impact of new help‑center content on ticket volume.

LLM/NLP for Support Analytics

Tests practical application of LLMs to classify, route, and analyze text at scale.

• Build a plan to classify tickets into 20 topics and 5 severities. Prompting or fine‑tune? Why?
• How would you evaluate classifier performance and address class imbalance?
• Propose a human‑in‑the‑loop QA workflow and feedback loop to improve the model.
• How do you monitor for drift after a major product launch?
• Discuss latency and cost trade‑offs for real‑time routing vs. batch tagging.

Operations Case and Product Sense

Examines how you translate analysis into decisions under time pressure.

• Ticket volume spikes 25% after a billing change. What’s your triage plan and first analyses?
• Propose a capacity model for support staffing given seasonality and launch calendars.
• Recommend actions to improve SLA attainment without adding headcount.
• Prioritize three different friction themes with limited engineering bandwidth.
• Outline an incident review memo: structure, metrics, decisions, and owners.

Communication and Influence

Assesses your ability to drive alignment and adoption.

• Present a 5‑minute readout on support health for the past month. What’s the story?
• Push back on a requested metric that is misleading. How do you handle it?
• Two BPOs underperform on different KPIs. How do you tailor scorecards and action plans?
• How do you ensure dashboard adoption and reduce ad‑hoc requests?
• Describe a time you changed a decision with data despite initial resistance.

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.

8. Frequently Asked Questions

Q: How difficult is the interview, and how much time should I prepare?
Expect a high bar, with emphasis on real‑world analytics, SQL fluency, and crisp storytelling. Most candidates benefit from 2–4 weeks of focused prep: daily SQL drills, 3–5 BI/metrics design reps, and several full‑length case run‑throughs with feedback.

Q: What differentiates successful candidates?
They combine expert SQL and metric rigor with practical product/operations judgment. Strong candidates deliver decision‑grade narratives quickly, defend governance choices, and show how LLM‑powered workflows shift outcomes.

Q: How fast is the process and what’s the typical timeline?
Timelines vary by team needs, but many candidates complete the process within 2–4 weeks once interviews begin. Your recruiter will share the precise sequence and any adjustments for seniority or location.

Q: Will I need to code in Python/R live?
You will primarily be tested on SQL and case‑based analytics. Some teams include a lightweight Python/R segment or expect you to describe how you’d implement analyses, quality checks, or automation.

Q: What is the working model for this role?
This role is San Francisco‑based with a hybrid schedule (typically 3 days/week in office). Relocation assistance is offered for new employees.

Q: How much emphasis is placed on LLMs for this role?
High. You should be able to design and evaluate LLM‑based classifiers for ticket tagging/sentiment, articulate prompt vs. fine‑tune trade‑offs, and plan for drift monitoring and human‑in‑the‑loop QA.

9. Other General Tips

• Anchor to outcomes, not artifacts: When presenting dashboards or classifiers, start with the decisions they enable and the behaviors they change. Interviewers reward impact framing.
• Name and tame ambiguity: State assumptions explicitly, propose fast validation steps, and show how you would derisk unknowns in the first week.

• Operationalize governance: Don’t just define metrics—assign ownership, refresh cadences, and quality checks. Explain rollout, training, and adoption tracking.
• Think in tiers: Design dashboards with executive, manager, and agent views. Build drill paths that connect KPIs to root causes without data thrash.

• Make LLMs measurable: For classification, specify labeled test sets, thresholds by class, and cost/latency budgets. Show how you’d monitor drift and trigger re‑training.
• Narrate trade‑offs: When speed and accuracy conflict, offer options with risks and a recommendation. Close with owners and next steps to demonstrate leadership.

10. Summary & Next Steps

The Data Analyst (User Operations) role at OpenAI is an opportunity to shape how millions of users experience cutting‑edge AI. You will define service‑health metrics, operationalize LLM‑powered insights, and build self‑serve systems that keep the organization ahead of demand. It is hands‑on, high‑leverage work that rewards clarity, speed, and craftsmanship.

Center your preparation on five pillars: SQL mastery, metrics/BI governance, support analytics fluency (SLAs, FCR, deflection), LLM‑powered classification, and decision‑grade storytelling. Practice realistic cases end‑to‑end—queries, metrics, dashboard design, and the executive readout—to mirror how the interview probes depth and execution.

Focused, deliberate practice will materially improve your performance. Sequence your prep, seek feedback on full cases, and rehearse concise narratives that move decisions. For more interview insights and role‑specific resources, explore Dataford. You are preparing for a rigorous process, but with the right plan and reps, you can excel and make an immediate impact on OpenAI’s user experience.

This module summarizes compensation components (base salary, equity, and sometimes bonus) and how they map to seniority. Interpret ranges as guidance; actual offers vary by level, experience, and location. Use it to calibrate expectations and prepare thoughtful, data‑backed compensation discussions.