Senior Business and Marketing Analyst

PeopleFinders

2d•$85,000 - $100,000

About The Position

Are you passionate about data and helping businesses make smarter decisions? We're looking for a Senior Business and Marketing Analyst to join our team and help us better understand our customers, evaluate what's working, and find new opportunities to grow. You'll collaborate closely with marketing, product, and engineering teams — and your work will have a direct impact on how we move the business forward. We're in an exciting phase of growth — modernizing our data infrastructure and building out our analytics capabilities. You'll be joining at the right time to help shape how we work with data, contribute to a reporting environment that's still maturing, and leave a real mark on how the organization uses insights to make decisions. Our core focus is D2C/B2C on PeopleFinders.com and across our network of high-traffic websites, with a growing B2B presence for multiple brands, adding an exciting new dimension to the work.

Requirements

5+ years of experience in a data analyst or similar role
Strong proficiency in SQL (query optimization, complex joins, window functions, CTEs)
Experience with R or Python for statistical analysis and predictive modeling
Analyze paid acquisition performance across search, display, social, and affiliate channels connecting ad platform metrics (CPA, CTR, impression share, etc.) to downstream conversion events and unit economics
Hands-on experience building data models and visualizations in Domo, Tableau, Power BI, and extensive experience with Domo is a plus!
Experience with Snowflake (preferred) and/or AWS (Redshift, S3, Glue, etc.) for cloud-based data warehousing and processing
Demonstrated experience with subscription/trial-based business models (trial conversion, churn, LTV) in a high volume in a high-volume, D2C/B2C SaaS Subscription environment
Experience with performance marketing campaign analytics and channel performance (paid search, display, affiliates)
Experience building retention models and/or propensity-to-buy models
Experience building out forecast models
Experience with QA of large datasets
Solid background using Google Analytics 4 (GA4 360) and digital marketing analytics
Strong communication and data storytelling skills
Ability to work independently and manage multiple priorities in a fast-paced environment
Experience using AI/ML tools to augment analysis workflows — prompt engineering, copilot-assisted coding, or LLM-based data exploration

Nice To Haves

Experience in a B2B environment in addition to B2C
Experience with Recurly or similar subscription billing/revenue platforms
Experience with multi-touch attribution and flexibility between attribution models
Experience with ad platform APIs and Data Connectors
Experience working with large affiliate networks and partner-level performance data

Responsibilities

Design, develop, and maintain dashboards, reports, and data visualizations to support business decision-making
Perform end-to-end campaign analysis — measuring and trouble-shooting performance, attribution, and ROI across marketing and product initiatives
Analyze customer performance through segmentation, cohort analysis, lifetime value (LTV) tracking, churn analysis, and usage/engagement behavior to identify trends and growth opportunities
Build and maintain predictive models for customer retention, LTV, and propensity to buy, leveraging behavioral and transactional data
Hit the ground running in a maturing analytics environment — our core infrastructure is being modernized, and you'll play an active role in building out reporting, refining data models, and helping establish best practices in Domo as we migrate to the platform
Partner with the data engineering team to coordinate on Domo and ETL pipelines, ensuring data accuracy, availability, and alignment with analytical requirements
Translate business questions into analytical frameworks and communicate findings clearly to both technical and non-technical audiences
Identify trends, anomalies, and opportunities within large datasets to inform strategic decisions
Support data governance and documentation efforts to maintain data quality and integrity