S
Shiftsmart
Data Analyst (Supply)
Skills
SQL QueriesData EngineeringPythonData SciencepandasData Analysisdbt
17 hours ago
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Important information
Contract type:
Permanent
Salary:
$100,000 – $135,000 / year
Location:
New York, NY, USA
Work mode:
Onsite
Published on:
22 October 2026
What they need
- The mission of the Supply team is to manage Shiftsmart’s partner (or worker) supply to ensure that we have enough high-quality partners to fill shifts for our customers
- The Supply team is responsible for ensuring partners download the app, are prepared for their first shift, and have a positive onboarding and first shift experience
- The Data Analyst will build and maintain core data tables and models across the partner (worker) success, marketplace, and supply team to directly drive and increase fill rate and fulfillment quality across the company
- You will be instrumental in designing new business processes for scale, building and launching new initiatives, and optimizing existing ways of working
- Your role will evolve over time but some of your early responsibilities will include
- Scale the Supply Model: Build on the foundation of the existing supply model by expanding the concept of core DBT data tables that dynamically support decisions across the broader Partner (worker) organization. You will help turn data models into foundational infrastructure that allows the business to respond to real-world outcomes with less manual intervention
- Investigate and diagnose data anomalies and unexpected changes in business performance end-to-end. When something looks wrong you will trace the issue through our data pipeline to identify the underlying root cause and determine the appropriate solution
- Operational Analytics: build and maintain BigQuery analyses and core data tables that support day-to-day operational decisions to help the Supply team understand and improve performance. Including, the zone-level health dashboards, partner funnel breakdowns, fill-rate trend detection, cohort performance reporting, and other analyses.
- Maintenance and Reliability: own the maintenance, development, and expansion of core data tables in DBT across the Supply team. You will build sustainable, reusable data models that reliably power operational decisions across Supply, Marketplace, and Partner (worker) Success. Own the day-to-day upkeep of critical data systems and ensure recurring analyses and models remain accurate, reliable, and up to date as the business evolves
- Predictive Analytics: grow into developing and iterating on predictive models that help the business proactively identify and address operational risks including feature engineering, backtesting, model evaluation, and communicating results to stakeholders
- Cross-Functional Collaboration: partner with Supply, Marketplace, Partner Success, Product, and Engineering to understand business problems, translate them into data requirements, and build reliable data solutions that drive operational outcomes- Analytically-minded: You have 2-4+ years of experience in analytics, data science, data engineering, quantitative operations, or a similar analytical role. You are highly comfortable with SQL and can work through complex queries. You are comfortable in Python (pandas, notebooks) for analysis and modeling, and you have experience working with DBT or are capable of quickly learning it. You enjoy getting into the details of a dataset and figuring out what is really happening
- High ownership: You are self-directed and don’t wait for someone to tell you exactly what to do. You take a loosely defined problem, figure out what needs to happen, and drive it through to a solution
- Clear communicator: You can take a complex analysis or investigation and explain the key takeaway simply. You know how to communicate the “so what” to both technical and non-technical teammates
- Highly organized: You think in systems and process, relish bringing structure to ambiguity, ruthlessly prioritize, and feel organization is second nature to you
- Problem solver: You thrive working through and solving complex problems, you know how to turn an ambiguous question into a structured investigation and keep digging until you find the root cause
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