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1-800-Flowers Family of Brands

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Senior Data Solutions Engineer

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Summary

A senior analytics engineer on 1-800-Flowers' Data Products & Platforms team who builds and owns analytics-ready datasets in Snowflake (with Coalesce.io ELT), maintains semantic views to standardize KPIs, and enables self-service BI in Sigma and Power BI. The role also covers data quality, documentation, and preparing datasets for AI/advanced analytics.

We are seeking a Senior Data Solutions Engineer to join our growing Data Products & Platforms team. This role is designed for an experienced analytics professional who operates comfortably across analytics and data engineering, serves as a thought partner to the business, and takes ownership of delivering trusted, scalable, analytics-ready data products.

As a senior member of the team, you will play a key role in shaping the analytics data layer, defining standardized metrics and datasets, and enabling high-quality reporting and self-service analytics across the organization. You will work closely with business stakeholders, analytics partners, and data engineering teams to ensure data is reliable, well-documented, and ready to support advanced analytics and AI use cases.

What You’ll Do

  • Design, build, and own analytics-ready datasets in Snowflake using curated ELT pipelines (e.g., Coalesce.io)
  • Develop and maintain Snowflake semantic views to standardize business logic, KPIs, and metric definitions across teams
  • Act as a trusted partner to business stakeholders by translating analytical needs into scalable, reusable data solutions
  • Enable and support self-service analytics across BI tools such as Sigma and Power BI
  • Establish and maintain clear documentation for datasets, metrics, and analytical models
  • Proactively monitor, validate, and improve data quality, performance, and reliability
  • Collaborate closely with data engineering and platform teams to evolve and optimize the analytics data layer
  • Support AI and advanced analytics initiatives by delivering well-structured, high-quality datasets suitable for forecasting, machine learning, and generative AI
  • Contribute senior-level guidance on analytics engineering best practices, data modeling standards, and dataset design

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