Senior Data Analyst - Customer Analytics

MNTN

7h•Remote

About The Position

At MNTN, we put our people first, full stop. This allows our company culture to be defined by our team members and their shared values, like trust, ambition, quality, radical honesty, and compassionate leadership. We pride ourselves on bringing unrivaled performance and simplicity to Connected TV advertising. Our self-serve technology makes running TV ads as easy as search and social, helping brands drive measurable conversions, revenue, site visits, and more. We’re committed to innovation that empowers, not replaces. At MNTN, AI is a tool for growth, enhancing efficiency while keeping a people-first approach. Our goal is to streamline workflows and drive new solutions—without compromising the human element that makes our company great. So if wanting to do more, own more, and make a bigger impact comes naturally to you, then you may be the person we're looking for to join us in our next stage of growth. We’re looking for a Senior Data Analyst, Customer Analytics to join MNTN’s Business Analytics team. In this role, you’ll help drive how MNTN measures, understands, and improves customer outcomes across our connected TV advertising platform. You’ll work with advertising, campaign, account, product, and performance data to deliver insights, reporting, and analytical solutions that support better decision-making across the business. This role partners closely with analytics, engineering, product, and business stakeholders to translate complex data into actionable recommendations and scalable customer analytics capabilities.

Requirements

5+ years of experience in data analytics, business analytics, customer analytics, product analytics, marketing analytics, business intelligence, or a related field.
Strong SQL skills, with experience working in high-volume, large-scale data environments.
Experience building scalable data models, analytical datasets, and reporting solutions that support repeatable analysis and decision-making.
Proven experience analyzing customer behavior, campaign performance, product usage, retention, growth, or similar business outcomes.
Proficiency with Python for analysis, automation, data transformation, validation, or lightweight modeling.
Experience with Tableau or similar business intelligence and dashboarding platforms.
Working knowledge of ETL/ELT processes, data pipelines, and data modeling best practices.
Familiarity with statistical analysis, forecasting, segmentation, cohort analysis, experimentation, and predictive modeling techniques.
Strong problem-solving skills with the ability to translate ambiguous business questions into structured analytical approaches.
Excellent communication and stakeholder management skills, with the ability to explain complex analyses to technical and non-technical audiences.
Experience managing competing priorities and delivering results in fast-paced environments.

Nice To Haves

Experience in AdTech, MarTech, digital advertising, or media is a plus.

Responsibilities

Analyze customer behavior, campaign performance, product usage, account health, retention, and growth opportunities to identify actionable insights
Design and maintain scalable data models and analytical datasets that support customer reporting, segmentation, forecasting, benchmarking, and executive reporting
Build dashboards, recurring reports, and self-service analytics solutions that help stakeholders monitor performance and make informed decisions
Translate ambiguous business questions into structured analyses, clear findings, and practical recommendations
Apply analytical and statistical techniques, including segmentation, cohort analysis, forecasting, anomaly detection, and performance driver analysis, to solve customer-focused business problems
Partner with cross-functional teams to define metrics, improve data quality, and ensure consistent reporting across the organization
Validate analyses and reporting outputs through rigorous QA practices to maintain trust and accuracy
Communicate insights, methodologies, and recommendations effectively to both technical and non-technical audiences