Software Engineer, Data Platform
GlossGeniusHybrid - SF Bay Area$200k – $236kPosted 26 March 2026
Tech Stack
Job Description
About GlossGenius
GlossGenius is building an ecosystem enabling entrepreneurs to succeed. We empower small business owners to focus on being creators, not admins, by offering a range of business management tools including booking and scheduling, marketing, analytics, payment processing and much more.
Over 100,000 small business owners have chosen to rely on GlossGenius every day to run their entire set of business operations. Joining its powerful, intuitive platform with its vibrant, distinguished brand, GlossGenius is the ideal combination of a fintech, SMB software, and consumer company all in one.
About the Role
As we continue to scale, we’re strengthening our Data Engineering team. The primary focus of this role is to reimagine the data infrastructure to support our AI platform. This transformation will be critical to the success of our future products, and vital in delivering value to our 100,000+ business owners
You must be commutable to our NYC or SF headquarters. We default to being in-office 3-4 days per week with required attendance on Tuesdays and Thursdays.
What You’ll Do :
As an Software Engineer, Data Platform, you'll be both a hands-on technical expert and a strategic leader. You'll drive the design of core data models and pipelines in our Snowflake and Clickhouse lakehouse, setting the standards for quality, reliability, and scalability across the platform.
You'll own end-to-end solutions—from architecture and implementation to operations and optimization—while helping guide the long-term direction of GlossGenius' data ecosystem. You'll collaborate across teams to turn complex business problems into robust data solutions, mentor engineers to help them grow and deliver at a higher level, and help the team evolve toward a fully governed lakehouse with fine-grained access controls and consistent lineage.
Design and implement core data models and pipelines that power analytics, ML, and product experiences
Implement modern data lake orchestration patterns, including medallion architectures
Architect and evolve a scalable, cost-efficient, and reliable lakehouse foundation using Snowflake, Clickhouse, and orchestration tools
Define best practices and technical standards that improve data quality, governance, and performance across teams
Mentor engineers and foster a culture of ownership, operational excellence, and continuous learning
Shape the long-term technical vision and roadmap for GlossGenius' data platform
What We’re Looking For
5+ years of experience in data engineering, with a strong background in data architecture, data modeling, and distributed data systems
Deep expertise in modern lakehouse technologies such as Snowflake and Clickhouse
Experience implementing modern data orchestration patterns for big data use-cases, including batch and streaming workloads
Advanced proficiency in SQL and Python or Scala, including performance optimization and large-scale ETL design
Demonstrated ability to lead technical initiatives, set standards, and influence decisions across teams
Comfort owning systems end-to-end, including monitoring, reliability, and cost management
Excellent communication skills with the ability to translate technical trade-offs to both engineers and non-technical stakeholders
Benefits Perks
Flexible PTO
Competitive health dental insurance options, with premiums partially or fully covered by GG
In-person opportunities that are designed to help team members foster collaboration and build community (ie; working out of a co-working space, team dinners, and other team building activities)
Fertility and adoption benefits via Carrot
Generous, fully-paid parental leave policy
401k benefit - employees are eligible to contribute starting day 1 of employment
Professional Development - employees receive a yearly stipend for approved learning and educational-related expenses
Pre-tax commuter benefits
Dependent Care FSA
Home office support
The starting base salary for this role ... (truncated, view full listing at source)
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