Customer Manager - Quality & Insights

Hard Rock Digital•Hollywood, FL

9d•Hybrid

About The Position

As Customer Quality & Insights Manager, you will build and lead how Hard Rock Digital understands and acts on customer sentiment. This is a zero-to-one role: the function doesn't exist yet, and you will be the one to organize various efforts into one cohesive strategy — the reporting, the process, and eventually the team. At the center of the role is a closed loop, and running that loop well is the actual job: identify where sentiment is slipping, diagnose what's driving it, get the finding to the team who can fix it, and then track what happens after the fix ships to confirm it actually moved the needle. Anything short of closing the loop is just reporting; closing it is what makes the role valuable. You will organize diagnosis around three pillars that drive customer sentiment: Person (how interactions are handled), Product (how our app and platform perform for customers), and Policy (how our processes and rules affect the experience). Your job is to find the story in the data and hand stakeholders something specific they can act on — not to audit or score individual performance. When a finding touches an individual interaction, your role is to surface the pattern and hand it to the team that owns coaching or process change, not to run that conversation yourself. This role reports directly to the Senior Manager, Operations Strategy & Insights. There are no direct reports today, but as the function grows, so will this team. You will work alongside the existing BI Operations function — the two teams are parallel, and you'll partner closely with them on shared data and stakeholder context.

Requirements

6–9 years of experience in analytics, business intelligence, customer experience, or a related field, including experience owning a function, workstream, or reporting capability with significant ambiguity.
Proficient SQL skills, including the ability to write and join queries across multiple data sources (CSAT, NPS, support systems) to build reporting independently.
Experience building and maintaining dashboards or reporting (Tableau or similar) that stakeholders rely on directly.
Demonstrated ability to diagnose the underlying driver behind a pattern in customer feedback and turn it into a specific, actionable recommendation.
Experience using AI tools within an analytical workflow, including sentiment classification, text analysis, or LLM-assisted review of qualitative feedback.
Strong written and verbal communication skills, with the ability to turn analysis into a recommendation and land it with stakeholders who don't report to you.
Comfort working across both quantitative data (CSAT scores, trend lines) and qualitative material (reviews, transcripts, case notes).
Bachelor's degree in Analytics, Business, Statistics, or a related field, or equivalent work experience.

Nice To Haves

Prior experience building or standing up a new reporting function or capability from the ground up.
People management experience.
Familiarity with contact center or customer support operations and workflows.
Familiarity with Snowflake or a similar cloud data warehouse.
Python experience, particularly for text/sentiment analysis (pandas, NLTK, or similar).
Experience in iGaming, online sportsbook, casino, or a regulated industry environment.

Responsibilities

Stand up the customer sentiment/insights function: the reporting, the classification taxonomy, and the closed-loop process end to end.
Define and own how the loop works across the org — from issue identification through post-implementation impact tracking — so stakeholders know how to engage with findings and recommendations.
Build the case for future headcount and, if approved, onboard and manage a Quality & Insights Analyst.
Represent the Insights function in cross-functional forums as the org's point of contact for customer sentiment.
Build and maintain CSAT and sentiment reporting that gives stakeholders a clear, ongoing view of customer sentiment trends.
Design and evolve a sentiment taxonomy that reliably classifies feedback into the Person, Product, and Policy pillars.
Mine reviews, support tickets, surveys, and other qualitative feedback sources for sentiment signal, using AI tools to accelerate classification and pattern detection.
Track sentiment trends over time and proactively surface emerging issues before they show up as escalations.
When sentiment dips or a pattern emerges, dig into the underlying story: is it how an interaction was handled, a product friction point, or a policy/process design issue?
Distinguish one-off incidents from systemic patterns, and prioritize investigations toward the issues with the greatest sentiment impact.
Turn what you find into a clear, specific narrative stakeholders can act on — the goal is to explain what's driving the pattern, not to audit or score any individual's performance.
Route findings to the team best positioned to act on them (people leadership, product, policy/process owners) with a specific, actionable recommendation.
Maintain a running view of every open issue's status in the loop: identified, diagnosed, routed, and resolved.
Track outcomes after a fix ships to confirm whether it actually moved sentiment, and close the loop back to the original finding.
Present findings and loop status clearly to stakeholders, including team leads and cross-functional partners who own the Product and Policy pillars.
Build data-backed business cases when a recommendation requires investment, prioritization, or a change to an existing process.
Write and optimize SQL queries to extract, transform, and validate sentiment and CSAT data across large, complex datasets.
Validate data integrity and ensure sentiment classifications and diagnostic findings are grounded in accurate, well-understood sources.
Create and maintain clear documentation for sentiment definitions, classification methodology, and recurring reports.
Collaborate with the BI Operations team on shared data, metrics, and stakeholder context to ensure consistency across functions.