Description
365 Retail Markets is the most trusted global provider of unattended retail technology, delivering conveniently smart self-service solutions since 2008. The company’s all-in-one platform powers retail spaces across food service, global retail, and hospitality with a comprehensive suite of frictionless smart stores, micro markets, vending, catering, and dining point-of-sale options. These technologies can be found worldwide in corporate offices, manufacturing and distribution facilities, educational campuses, hotels, and beyond.
As a nine-time honoree on the Inc. 5000 list of fastest-growing companies in the United States, and with a continually expanding global presence, 365 Retail Markets is committed to growth, innovation, and providing superior, integrated technology that meets the evolving needs of its customers and consumers.
Job Overview
We are seeking a highly skilled, independent, and ambitious Senior Data Analyst who thrives in a fast-paced, data-driven environment. This role requires someone who can analyze large datasets, identify patterns and trends, and suggest key performance indicators (KPIs) that drive business decisions. You will collaborate closely with the Senior Product Manager to co-develop and enhance data products such as dashboards, KPIs, ad-hoc BI reports, data pipelines, visualizations etc.
Responsibilities
- Independently analyze large and complex datasets from multiple internal and external sources (e.g., SQL databases, Snowflake, APIs), identifying actionable insights and trends.
- Collaborate with the Senior Product Manager and cross-functional development teams to design and develop scalable data products.
- Proactively suggest, track, and refine KPIs to monitor and improve business outcomes.
- Conduct in-depth statistical analyses using Python or R, including regression, trend analysis, and predictive modeling.
- Present clear, data-driven insights to both technical and non-technical stakeholders, influencing business decisions.
- Create comprehensive documentation and standards for data analysis, workflows, and product development.
- Support ad-hoc data analysis requests and deep dives, ensuring data-driven decision-making across departments.
- Play a key role in the ongoing evolution of data products and strategies, contributing to the company's overall data vision
Growth Opportunity
This role is designed for growth. As the Senior Data Analyst, you will have the chance to:
- Lead Product Development: Take full ownership of data products, including KPIs, dashboards, and data models, with a focus on delivering high-impact solutions.
- Drive Strategy: Influence data strategy by working closely with stakeholders to understand business needs and align data solutions with overall goals.
- Expand Scope: Eventually transition into a Data Product Owner role, where you will define product roadmaps, manage stakeholder expectations, and lead data-driven projects from concept to completion.
Requirements
- Expertise in SQL and Snowflake, with extensive experience in querying and transforming large datasets.
- Advanced skills in Python or R for data analysis, automation, and statistical modeling.
- Proven ability to work independently, managing the end-to-end data analysis process, from data extraction to insight delivery.
- Experience working with APIs and external data sources to enrich internal datasets.
- Strong ability to identify trends, propose KPIs, and generate actionable insights from complex data.
- Attention to detail and accuracy in data handling, ensuring data quality across all products.
- Excellent communication skills, able to translate technical findings into business insights for a variety of stakeholders.
- Proactive problem-solving mindset with the ability to manage multiple priorities in a fast-paced environment.
- Curiosity and initiative in exploring new data sources, tools, and technologies.
Preferred Skills
- Experience in data visualization tools (Power BI, Tableau) for building dynamic, insightful dashboards.
- Experience in Consumer-Packaged Goods (CPG) or a related industry (preferred but not required).
- Strong understanding of data science techniques such as regression analysis, forecasting, and machine learning models.
- Familiarity with cloud-based data environments and working with data engineering teams to optimize workflows.
- Experience in automating data processes and creating scalable data pipelines for various use cases in partnership with engineering teams.
- Knowledge of data governance, data security, and best practices in data management.
