Senior Program Manager, Quality Assurance

Instacart

1d•$122,000 - $155,000•Remote

About The Position

Instacart is seeking a highly motivated program manager who is also a hands-on data analyst to operate the Quality Assurance program for Instacart’s global Customer Experience organization. This role is equal parts program management and analytics. You will own the operating rhythm of the QA program — cadences, service-level agreements, and cross-functional commitments — and you will personally build the dashboards, reporting, and analyses that turn raw audit data into insight the business acts on. This role requires excellent time-management, effective communication skills for engaging with stakeholders at all levels, strong SQL and dashboarding skills, and a passion for translating signals into measurable action. The Quality Assurance team within Customer Experience is responsible for ensuring every customer, retailer, and shopper interaction meets the bar we set for the global CX organization. We operate the feedback loop that turns support interactions into measurable improvements across the business: we evaluate quality across every channel, analyze trends and outliers to provide early warning, and route every signal to a named owner across five action workstreams (performance management, learning and development, automation, process, and product feedback). By fostering strong cross-functional partnerships with Product, Engineering, Operations, Legal, and L&D teams, we drive both quality and efficiency outcomes for CX. This role reports to the Sr. Manager, Quality Assurance, who oversees the QA strategy and execution across all CX pillars. The Senior Program Manager will work closely with Performance Management, Learning & Development, Automation Engineering, Operations, Policy, Product, and the broader Analytics organization at Instacart. This role focuses on utilizing audit data and program management discipline to drive quality, efficiency, and contact-prevention outcomes across Customer Experience — spanning everything from the live customer interaction to executive reporting on business impact.

Requirements

Minimum 6–8 years of combined program management and analytical experience, preferably in customer experience, contact center operations, trust and safety, or comparable operational functions.
Experience in Customer Experience, contact center, quality assurance, or trust and safety operations.
Understanding of contact center metrics (quality scores, sentiment, first contact resolution, average handle time) and the operational levers that move them.
Understanding of A/B testing and other forms of statistical analysis.
Proficiency with AI tools (e.g., Claude, ChatGPT, Copilot) and a demonstrated ability to integrate them into day-to-day workflows.
Demonstrated experience as both a program manager and a hands-on data analyst — not one supported by the other.
High proficiency in SQL, with experience writing complex queries, joins, and optimizations against large datasets.
Experience with analytical visualization tools such as Mode, Tableau, Looker, Sigma, or similar tools.
Track record of building reporting and analytics that an executive audience actually uses to make decisions.
Proven ability to run cross-functional programs with named owners, published service-level agreements, and measurable outcomes.
Ability to identify potential root causes contributing to changes in quality and efficiency metrics and provide recommendations on mitigation strategy.
Extremely strong verbal and written communication skills, including the ability to synthesize complex topics and create compelling narratives for various audiences.
Ability to work effectively with internal stakeholders, including data scientists, data engineers, and operational leaders. Work cross-functionally with Product, Engineering, Operations, and L&D to drive change.
Excellent teamwork skills and desire to help others learn.
High level of accountability and ownership — driven and focused self-starter.
Strategic mindset — the ability to think ahead of where the program is at now and help stand up a new operating model rather than maintain an established one.

Nice To Haves

Familiarity with QA operations: rubric design and calibration, auditor variance management, dispute workflows, and core quality scoring methodologies.
Working knowledge of QA platforms such as Kaizo, MaestroQA, Playvox, or comparable tools.
Exposure to LLM-assisted auditing, automated quality scoring, or other applied AI tooling within a customer experience context.
Experience with R or Python (fluency in at least one preferred).
Experience with experimentation, data modeling, ETL, and data pipeline development.

Responsibilities

Operate the QA cadences end-to-end — daily anomaly standups, weekly quality reviews, monthly business reviews, and quarterly rubric calibration sessions. Maintain meeting hygiene: agendas, decisions logged, action items assigned with clear owners and due dates.
Manage the central tracker that captures every quality signal raised and routes it to a named owner across the five action workstreams. Publish and enforce service-level agreements; surface adherence (target ≥95%), escalation cycle times (target ≤7 days), and signal-to-action lag (target ≤5 days) to leadership weekly.
Collaborate with Engineering, Product, L&D, Automation, and Operations teams to ensure every quality signal has a destination, every action has an owner, and every outcome is measured.
Work with QA leadership, Product, Data Science, and cross-functional Analytics teams to understand quality trends, prioritize roadmap initiatives, and shape the future of AI-assisted auditing, rubric evolution, and contact-prevention work.
Regularly communicate outcomes and insights to cross-functional stakeholders, including senior leadership, to guide strategic decision-making and drive process and performance improvement.
Build and maintain QA dashboards and reporting that surface critical KPIs such as quality scores, customer sentiment, first contact resolution, average handle time, audit coverage, and action SLA adherence across the customer, retailer, and shopper pillars.
Write SQL queries against contact and audit data to investigate spikes, isolate root causes, identify auditor variance, and answer ad-hoc business questions from CX, Product, and Operations leadership.
Analyze audit data, customer sentiment, and contact-driver patterns to identify systemic issues, calibration drift, and opportunities for contact prevention.
Partner with Data Science, the broader Analytics organization, and QA leads to design dashboards and reporting models that provide a unified view of QA performance across contacts. Work closely with Operations and Legal teams to meet reporting requirements and audit needs.
Develop forward-looking analyses (“if we take action X, what should we expect in metric Y”) to support quarterly goal-setting and ROI assessment for QA-driven initiatives.
Build automated reporting systems to keep CX and operations leaders informed of trends, variations, and opportunities across regions, pillars, and channels.