Product Data Analyst, Mobile App User Acquisition

StackAdapt is the leading technology company that empowers marketers to reach, engage, and convert audiences with precision. With 465 billion automated optimizations per second, the AI-powered StackAdapt Marketing Platform seamlessly connects brand and performance marketing to drive measurable results across the entire customer journey. The most forward-thinking marketers choose StackAdapt to orchestrate high-impact campaigns across programmatic advertising and marketing channels.

StackAdapt’s Advertising Technology team is looking for a highly analytical and results-oriented Product Data Analyst to drive product and performance insights for our Mobile App User Acquisition program.

In this role, you will work closely with Product, Machine Learning, Business Intelligence, Data Engineering and business teams to define performance metrics, analyze campaign and product trends and translate complex data into actionable recommendations.

The primary focus of the role is product analytics, but we are looking for someone who can go beyond one-off analysis. Candidates with experience in analytics engineering or data engineering will be strongly preferred, particularly those who can build reliable datasets, data pipelines, dashboards and automated analytical workflows.

The ideal candidate has experience working with mobile app advertising, performance marketing or another data-intensive product environment. They should be comfortable working across structured and unstructured data sources and independently taking an analytical problem from initial question through data sourcing, analysis, visualization and recommendation.

We are also looking for someone with an AI-first mindset. Candidates who have experience using AI to accelerate analysis, automate repetitive workflows or build internal agents and reusable skills will be given preference.

• Analyze historical and real-time mobile user acquisition campaign data to identify trends, measure performance and uncover optimization opportunities
• Define, establish and refine the core metrics used to evaluate mobile UA campaign and product performance
• Partner with Product and Machine Learning teams to analyze targeting, bidding, attribution and optimization performance
• Conduct deep-dive analysis into campaign delivery, conversion funnels, user quality, attribution and return on advertising spend
• Build and maintain Tableau dashboards for performance monitoring, troubleshooting and product analysis
• Create dashboards and reporting structures that allow Product, Engineering, Sales and Account Management teams to independently investigate performance
• Lead weekly performance reviews and communicate key findings, risks and recommendations to stakeholders
• Prepare quarterly business reviews and leadership updates covering performance trends, product opportunities and areas requiring investment
• Work with Data Engineering, BI and Analytics teams to design scalable datasets and reporting solutions
• Build or maintain Apache Airflow pipelines that consolidate data from multiple internal and external systems
• Work with Snowflake, MySQL, APIs, logs, event-level data and other structured or unstructured data sources
• Develop reusable data models and curated datasets that support product analytics, experimentation and operational reporting
• Identify gaps in data quality, event instrumentation and reporting and partner with Engineering teams to address them
• Support product experiments by defining success metrics, evaluating results and communicating recommendations
• Automate repetitive reporting, investigation and analysis workflows using AI and other modern tools
• Build internal AI agents, reusable skills or analytical tools that improve data discovery, troubleshooting and team productivity

• Bachelor’s degree in Data Science, Business Analytics, Statistics, Computer Science, Engineering or a related field
• 3 or more years of experience in product analytics, data analytics, business intelligence or a related role
• Strong SQL skills and experience querying large, complex datasets
• Experience defining product or business metrics and converting ambiguous questions into structured analytical approaches
• Experience building dashboards and data visualizations in Tableau, Looker, Power BI or a similar platform
• Experience working with a cloud data warehouse such as Snowflake
• Experience working with relational databases such as MySQL
• Ability to independently investigate data issues, identify root causes and translate findings into clear recommendations
• Strong communication and presentation skills, with the ability to explain complex analysis to both technical and non-technical audiences
• Strong attention to data quality, metric consistency and analytical accuracy
• Ability to collaborate across Product, Engineering, Machine Learning, BI and business teams

• Experience in analytics engineering or data engineering
• Hands-on experience building or maintaining pipelines using Apache Airflow
• Experience building reusable data models, analytical tables or curated datasets
• Experience with Python for data analysis, automation or pipeline development
• Advanced experience building interactive Tableau dashboards for monitoring and troubleshooting
• Experience with ThoughtSpot or another self-service analytics platform
• Experience integrating data from APIs, event streams, logs and unstructured data sources
• Experience working with Mobile Measurement Partners such as AppsFlyer, Adjust, Singular or Kochava
• Understanding of mobile UA and performance marketing metrics, including CPI, IPM, click-to-install rate, ROAS, retention and lifetime value
• Familiarity with mobile attribution models, view-through attribution, click-through attribution and privacy-focused measurement frameworks
• Experience supporting machine learning models, experimentation or model-performance monitoring
• Experience working in advertising technology, mobile user acquisition, performance marketing or another high-volume data environment
• Experience using AI tools to accelerate analytics, data engineering, documentation or troubleshooting
• Experience building AI agents, reusable skills, MCP-based tools or AI-powered analytical workflows