Staff/Senior Analytics Engineer, Data Science & Analytics (DSA)

Accountabilities:

Design, build, own, and maintain reliable, production-grade analytics pipelines, ensuring robust monitoring, alerting, and continuous improvement.
Transform raw data into structured analytical models using dbt and Kimball dimensional modeling principles.
Manage the complete lifecycle of analytics features, from scoping and technical design through development, deployment, and maintenance.
Drive cross-functional projects by proactively identifying blockers, coordinating stakeholders, communicating technical trade-offs, and ensuring timely delivery with minimal supervision.
Leverage AI technologies, including large language models (LLMs), AI agents, and coding assistants, to improve the efficiency, quality, and speed of analytical development.
Translate retail operational data, including inventory levels, on-shelf availability, and task execution signals, into actionable insights that support internal decision-making and client business objectives.
Establish and promote best practices for data quality, reproducibility, documentation, testing, and code quality across analytics and data science workflows.
Contribute to the development and prototyping of conversational data experiences, including AI-powered agents and chatbot interfaces that allow users to explore and query data through natural language.
Requirements:
At least 5 years of experience in analytics engineering or a closely related field, with a proven track record of delivering and maintaining production-ready data features.
Strong hands-on experience with dbt, including developing data transformation models, implementing tests, and maintaining technical documentation.
Solid understanding of Kimball dimensional modeling and best practices for building scalable analytical data structures.
Advanced SQL skills and experience working with large-scale cloud data platforms, preferably Google Cloud Platform (GCP) and BigQuery.
Demonstrated ability to manage the full lifecycle of analytics solutions, including project scoping, development, deployment, and ongoing maintenance.
Strong cross-functional collaboration skills, with experience working alongside Engineering, Product, Data Science, or Commercial teams and effectively communicating technical decisions and trade-offs.
Previous experience in the retail industry is strongly preferred, particularly in store operations, inventory management, merchandising, or supply chain.
Practical experience using AI tools, including LLM APIs, coding assistants, and prompt engineering, to accelerate analytical and engineering workflows.
Strong ownership mentality, autonomy, problem-solving abilities, and the capacity to move complex projects forward in a fast-paced environment.
Excellent communication skills and the ability to translate complex technical concepts and data insights into clear, actionable information.
Preferred qualifications:
Familiarity with data pipeline orchestration tools such as Airflow or equivalent technologies.
Experience with data and model monitoring, CI/CD practices, and automated testing for analytical workflows.
Knowledge of data visualization platforms such as Looker, Tableau, or similar tools to communicate insights effectively to non-technical stakeholders.
Experience designing or prototyping AI-powered conversational interfaces for data exploration and analysis.
Benefits:
Annual base salary: $130,000–$165,000, depending on geographic market, relevant experience, technical expertise, and other objective business considerations.
Equity compensation: Opportunity to participate in the company's ownership through equity, subject to applicable eligibility and compensation arrangements.
Comprehensive benefits package: Access to a range of medical, financial, and other employee benefits.
Total rewards approach: Compensation considers base salary, potential equity, incentives, and well-being support.
Remote flexibility: Opportunity to work remotely, with the role also open to candidates in the San Francisco Bay Area.
Meaningful ownership: Take responsibility for production systems and features that directly influence retail operations and business performance.
High-impact environment: Work in a focused team where contributions are visible and technical expertise is valued.
Large-scale data challenges: Work with extensive retail datasets covering thousands of stores and billions of shelf observations.
Innovation-driven environment: Gain exposure to advanced AI, robotics, and data technologies shaping the future of retail.
Collaborative culture: Join a team that values transparency, empathy, agility, innovation, accountability, and customer-focused results.
Professional growth: Enjoy opportunities to influence technical standards, drive strategic initiatives, and expand your expertise in analytics engineering and AI.