Senior Data Analyst

Required Skills & Experience • 5+ years of experience in the data analytics/engineering space • Strong SQL expertise - complex joins, multi source queries, and window functions, with the ability to connect SQL logic back to business questions • dbt experience - building, maintaining, and updating data models and using Git/version control for dbt workflows • Familiarity with modern orchestration (Dagster, Airbyte, or equivalent) • ETL & Data Pipelines - solid understanding of ETL workflows and data pipelines • Experience working with ERPs (i.e. NetSuite), or Shopify/POS systems • Experience with working in a consumer goods or retail industry • Technical Collaboration – basic understanding of AWS and modern data infrastructure to support effective teamwork Nice to Have Skills & Experience • Exposure to BigQuery (limited usage planned) • Broader cloud experience (AWS preferred; GCP familiarity is a bonus) • Prior coursework or background in data science, ML, or forecasting • Interest in AI use cases within analytics and data workflows Job Description Overview A client in the luxury fashion space is looking for a Data Analyst/Engineer hybrid. This role sits at the intersection of business, data, and engineering. The individual will partner closely with Finance, B2C, eCommerce, Retail, and Digital Marketing stakeholders to understand their needs, translate requests into data requirements, and work with Data Engineering to deliver reliable data models and dashboards. The role is highly SQL driven with a strong emphasis on debugging, data modeling (dbt), and system understanding. Key Responsibilities Stakeholder & Business Partnership • Act as the primary analytics partner for Finance, B2C, eCommerce, and Retail teams. • Own intake and tracking of analytics requests, documenting and managing them in Jira. • Identify stakeholder needs, blockers, and data gaps; determine the appropriate next steps and owners. Data & Analytics Work • Build and maintain dashboards using data pulled from multiple systems. • Perform data cleaning, testing, and validation primarily using SQL. • Quickly identify issues in dashboards or models and trace errors back to the source system or dbt model. • Reverse engineer existing logic where documentation is limited to understand how data maps to the business. • Load curated datasets into Tableau and explore opportunities for enhanced visualization, customization, and interactivity. Data Modeling & Engineering Collaboration • Work hands on with dbt models: build, modify, and maintain data models to support business requests. • Collaborate closely with Data Engineers, who handle backend work and new system integrations. Systems & Integrations • Support and analyze data from systems such as Shopify, POS, and Teamwork. • Develop a strong understanding of how data behaves within each system and how it is ingested into the warehouse. • Serve as the bridge between business users and technical teams, ensuring requests are well scoped and actionable. Forecasting & Advanced Analytics • Some use of Python; forecasting work (e.g., revenue by store/day) is mainly internal and ad hoc. • May support or be assigned AI related tasks as they arise.