Data Specialist

Job Description We're looking for a detail-oriented, SQL- and Python-adept analyst who will serve as the connective tissue between our data and the people who use it. This isn't a software engineering role — it's for someone who genuinely loves knowing what data lives where, why it's structured the way it is, the processes used to generate and store it, and how to help others get the most out of it.What you'll do:You'll be the go-to resource for quants who need help pulling data — showing them where to look, constructing sample queries and notebooks to guide their exploration, and building out generalized tooling for data retrieval. You'll own documentation and organization of our datasets: what tables exist, what they contain, how they relate, and what conventions make sense given how the data gets used downstream.You'll also own the maintenance of our data pipelines — writing and updating the code that moves and transforms data, handling the cleaning and normalization work that comes with ingesting data from varied sources, and keeping things running reliably over time. When improvements to storage or access need to be deployed, you'll work closely with our DevOps team to make that happen.What we're looking for:A systematic, organized mind — you think in primary keys, data types, and relationshipsComfort mapping and documenting data landscapes across multiple systemsRelative SQL fluency, including a real understanding of query efficiency and optimizationProficiency in Python (or similar) for data pipeline work — cleaning, transforming, and moving dataEnough technical range to set up and maintain orchestration jobs (e.g., Airflow)Strong communication skills; you'll regularly translate between technical and non-technical stakeholdersPredictable and reliable availabilityNice to have:Experience in a data-intensive industry (finance, sports, ad tech, etc.)Familiarity with columnar or analytical databasesBackground in data modeling or schema designAbility to use AI tools to improve the efficiency of querying internal data