Lead Analyst - Data Operations

Role Overview

You will lead the day-to-day operation of a Data Desk, balancing request management with technical quality control and coaching early-career analysts. By keeping work on track, reviewing analytical outputs, and turning recurring requests into reliable self-service tools, you will help business teams get timely, accurate data while reducing avoidable manual work.

Key Responsibilities

• Manage the incoming L1 and L2 request queue, assess urgency and scope, and assign each ticket to the right analyst or pod lead.
• Classify requests across Logic, Exports, Ops, and Finance, and maintain clear routing guidance as demand and request types change.
• Track queue health and service-level performance, identify risks early, and raise blockers to the Head of Data Ops before deadlines are missed.
• Spot patterns in repeat requests and work with pod leads to develop dashboards or automated pipelines that give stakeholders a self-service alternative.
• Coordinate with Analytics Engineering to ensure automated outputs meet data quality and governance standards.
• Review every entry-level analyst deliverable before it reaches a business stakeholder, checking SQL, LookML, and dashboard logic for accuracy, efficiency, and compliance with the Data Standard.
• Lead daily team stand-ups, hold regular one-to-ones with direct reports, and support progress against development plans agreed with the Head of Data Ops.
• Introduce new Data Academy participants to the team’s workflows, tools, and quality expectations, and represent the Data Desk in cross-functional meetings when the Head of Data Ops is unavailable.

Requirements & Qualifications

• Bachelor’s or master’s degree in Computer Science, Data Science, Statistics, or a related technical discipline.
• Four to nine years of experience in data, analytics, or data operations.
• At least one to two years of responsibility for reviewing work, mentoring, or managing junior data analysts.
• Strong practical SQL skills and the ability to identify issues in analytical logic and query performance.
• Working knowledge of LookML or comparable BI-layer logic, with experience in tools such as Looker, Tableau, Power BI, or Metabase.
• Familiarity with AWS-based data environments.
• Demonstrated ability to replace recurring manual data work with automated processes or self-service solutions.