opengreenhousegreycroft
Senior Data Engineer
Thrive Market
LocationPlaya Vista, CA or Remote, Thrive Market HQ
WorkplaceFull
Last observed2026-06-13 05:24:07.190474
Job idgreycroft-thrive-market:greenhouse:4233437009
ABOUT THRIVE MARKET Thrive Market was founded in 2014 with a mission to make healthy and sustainable living easy and affordable for everyone. As an online, membership-based market, we deliver the highest quality healthy, and sustainable products at member-only prices, while matching every paid membership with a free one for someone in need. Every day, we leverage innovative technology and member-first thinking to help our over 1,700,000+ members find better products, support better brands, and build a better world in the process. We are also a Certified B Corporation, a Public Benefit Corporation, and a Climate Neutral Certified company. Join us as we bring healthy and sustainable living to millions of Americans in the years to come. THE ROLE Thrive Market’s Data Engineering team is seeking a Senior Data Engineer! We are looking for a brilliant, dedicated, and hardworking engineer to help us build high-impact products alongside our Data Strategy Team. Our site sees millions of unique visitors every month, and our customer growth currently makes us one of the fastest-growing e-commerce companies in Los Angeles. We are looking for a Senior Data Engineer with hands-on experience working on structured/semi-structured/Complex data processing and streaming frameworks. We need your amazing software engineering skills to help us execute our Data Engineering & Analytics initiatives and turn them into products that will provide great value to our members. In this role, we are hoping to bring someone in who is equally excited about our mission, learning the tech behind the company, and has the ability to work cross-functionally with other engineering teams. RESPONSIBILITIES Work across multiple projects and efforts to orchestrate and deliver cohesive data engineering solutions in partnership with various functional teams at Thrive Market Be hands-on and take ownership of the complete cycle of data services, from data ingestion, data processing, and ETL to data delivery for reporting Collaborate with other technical teams to deliver data solutions that meet business and technical requirements; define technical requirements and implementation details for the underlying data lake, data warehouse, and data marts Identify, troubleshoot, and resolve production data integrity and performance issues Collaborate with all areas of data management as lead to ensure patterns, decisions, and tooling are implemented in accordance with enterprise standards Perform data source gap analysis and create data source/target catalogs and mappings Develop a thorough knowledge and understanding of cross-system integration, interactions, and relationships in order to develop an enterprise view of Thrive Market’s future data needs Design, coordinate, and execute pilots/prototypes/POC to provide validation on specific scenarios and provide an implementation roadmap Recommend/Ensure technical functionality (e.g. scalability, security, performance, data recovery, reliability, etc.) for Data Engineering Facilitate workshops to define requirements and develop data solution designs Apply enterprise and solution architecture decisions to data architecture frameworks and data models Maintain a repository of all data architecture artifacts and procedures Collaborate with IT teams, software providers, and business owners to predict and devise data architecture that addresses business needs for collection, aggregation, and interaction with multiple data streams QUALIFICATIONS Excellent communication and presentation skills (verbal, written, presentation) across all levels of the organization. Ability to translate ambiguous concepts into tangible ideas. Hands on experience programming in Python, DBT and Airflow Expertise with RDBMS and Data Warehousing (Strong SQL) with Snowflake or similar In-depth knowledge and experience with data and information architecture patterns and implementation approaches for Operational Data Stores, Data Warehouses, Data Marts and Data Lakes Exp
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