we are looking for a Senior Data Analyst.
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 Data Analyst 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: 5+ years in data analytics / data research with large-scale datasets
3+ years hands-on Python and PySpark at scale
Solid applied statistics - sampling and estimation, bias and coverage, significance, uncertainty - and the ability to explain limitations to non-technical audiences
Demonstrated ownership of data quality: metrics and validations for datasets others depend on
Proficiency with AI-assisted coding (Claude Code, Cursor, Copilot), with judgment about when to trust, verify or discard output
Excellent written and spoken English; comfortable presenting to customers
Ability to own deliverables independently across multiple teams under tight deadlines
B.Sc. in a quantitative field; M.Sc. / MBA or academic research experience - advantage
Technical Skills: PySpark (must, with performance awareness), Databricks or equivalent Spark platform, Airflow in production, Terraform / IaC, Git-based collaborative development
This position is open to all candidates.