Analytics Engineer

ABOUT PLACER.AI**:** 

Placer.ai is transforming how organizations understand the physical world. Our location analytics platform provides unprecedented visibility into locations, markets, and consumer behavior. Placer empowers thousands of customers—from Fortune 500 companies, to local governments and nonprofits— to make smarter, data-driven decisions.
What sets us apart? We've built the most advanced location intelligence platform in the market while maintaining an uncompromising commitment to privacy, proving that powerful analytics and responsible data practices can coexist.
Our growth reflects the market's demand: we reached $100M in annual recurring revenue within just 6 years of launching, achieved unicorn status with a $1B+ valuation in 2022, and continue to expand rapidly as one of North America's fastest-growing tech companies. We're creating a $100B+ market opportunity, and we're just getting started.
Named one of Forbes America's Best Startup Employers and a Deloitte Technology Fast 500 company, we're building a culture where innovation thrives, collaboration is the norm, and every team member contributes to reshaping how the world understands location.

SUMMARY:

The Custom Feeds team turns Placer's data into production-grade bespoke data products for our largest customers — tailored to specific business questions and shipped on a recurring basis. We're looking for a Senior Analytics Engineer, who is both a pipeline builder and a data researcher. You'll own custom recurring feeds end to end — from methodology for new metrics, through pipeline design, to final acceptance before delivery. You'll lead conversations with internal stakeholders (US-based customer-facing teams and colleagues across R&D) and help generalize recurring logic into reusable models, metrics and packages the whole team builds on. Ideal for a rigorous, independent thinker looking for a delivery-oriented role where impact is tangible.

Responsibilities

• Own custom feeds from requirements through delivery and ongoing maintenance
• Develop methodologies and acceptance criteria for new metrics
• Build reproducible, tested production pipelines in PySpark on Databricks, orchestrated with Airflow and Terraform
• Partner with Solution Engineers and customer-facing teams on timelines, methodology and customer feedback
• Build reusable components and packages; collaborate with R&D and Data Science to reuse existing datasets, models and products — and make your work reusable by others

Requirements