Senior Data Engineer, Analytics

About the role

About the Team:

Grindr is an AI-native platform powering how millions of gay people connect globally. With 15M+ monthly users, 130B+ annual messages, and a team of fewer than 200, we move fast, stay lean, and tackle technical problems at a scale few companies ever see.

The Data Engineering team turns raw product data into the trusted datasets that power decisions across Grindr, from dashboards and experimentation to machine learning models and features in the app itself.

And we’re pushing that further than most. Every company wants an AI-powered data analyst; we’ve already built one. Data Daddy is our production AI analyst, used daily in Slack by employees and senior leadership to answer real business questions, with visibility all the way up to our CEO. It’s only as good as the data behind it, and that’s where this team comes in.

We’re looking for a Senior Data Engineer, Analytics to own data for our newest product features: building the data models, metrics, and semantic layers that data scientists, ML engineers, and Data Daddy all rely on. You’ll partner closely with stakeholders across the business and help shape how data is used across the organization.

At Grindr, we operate in Grindr Mode. Moderately hardcore day to day, truly hardcore when it counts. It’s about doing great work without burning out. Outcomes over outputs. Smart, driven people who raise your bar, with room to live full lives.

This is a hybrid role based in our Chicago, SF, or Palo Alto office and will require you to be in person Tuesdays and Thursdays.

About the Job:

- Own Our Newest Features: Take end-to-end ownership of critical business datasets, from understanding stakeholder requirements and designing data models to building pipelines, validating metrics, and ensuring data quality.

- Build AI-Ready Analytics: Design and develop reliable, well-structured data models that power analytics, reporting, and AI-driven tools like Data Daddy, enabling employees to ask complex business questions using natural language.

- Develop the Semantic Layer: Create and maintain reusable metrics, dimensional models, and semantic definitions that make data consistent, discoverable, and accessible to both human analysts and AI agents.

- Partner Across Teams: Work closely with Data Science, ML Engineering, Product, and Analytics to translate business questions into scalable data solutions that support decision-making, experimentation, and machine learning.

- Leverage Modern Engineering Tools: Use AI-assisted development tools and modern data engineering practices to accelerate development and raise the bar on how we model, test, and document data.

Role Requirements:

- Analytics Engineering Fundamentals: Strong SQL skills and experience designing dimensional data models, defining business metrics, and building datasets that support complex analytical questions.

- Data Engineering Experience: Experience turning raw, high-volume event data into reliable production datasets, including applying business logic, building and maintaining transformations, and validating data quality in a modern cloud warehouse with orchestration tools like Airflow.