We are looking for a Senior Data Analyst to join the Foundation Entities team, part of the Ingestion division.
The Foundation Entities group is where data meets reality. We model POIs (venues and complexes), chains, markets, and other foundation entities, tackling the challenge of turning billions of signals into a living, evolving database of the physical world. Our work powers the insights, products, and innovations that define us.
This role goes beyond traditional analytics. You will work with large-scale datasets, apply advanced analytics and statistical methods, and design scalable solutions that replace manual processes with automated, data-driven services. You will collaborate closely with engineers, data scientists, and product stakeholders, contributing directly to the accuracy, scalability, and innovation of foundational data.
RESPONSIBILITIES:
We expect our analysts to be self-driven, capable of working independently with multiple stakeholders, and comfortable managing several projects in parallel.
Key responsibilities include:
Analyzing and improving the quality, coverage, and scalability of POIs, chains, and other foundation entities.
Developing metrics, monitoring, and validation tools to ensure data reliability and automation at scale.
Conducting applied research and testing hypotheses to replace manual workflows with data-driven processes.
Researching and integrating external data sources (business listings, APIs, open data, etc.) to enrich entity datasets.
Communicating insights and recommendations clearly to both technical and non-technical audiences.
Requirements: Strong data literacy: ability to connect real-world signals with large-scale data representations.
5+ years of experience as a data analyst working with large datasets in production.
Proficiency in Python and PySpark (pandas, numpy, etc.) for advanced data wrangling and analysis.
Hands-on experience with cloud-based environments (GCP, AWS, or Azure).
Strong communication skills to collaborate across engineering, product, and business stakeholders.
Preferred skills:
Familiarity with big data tools such as Spark.
Experience with entity resolution, anomaly detection, or time series analysis.
Working with geospatial data and its Python ecosystem.
Familiarity with BI tools (Tableau, Looker, PowerBI, etc.) as serving layers.
Exposure to machine learning frameworks (TensorFlow, PyTorch, or Scikit-learn).
B.Sc in a quantitative field (M.Sc a plus).
This position is open to all candidates.