Software Engineer, Data Systems
Gamma · On-site
MeritLog read this listing from Gamma's Ashby job board and last checked it on September 10, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
Job details
- Work model
- On-site
- Salary
- Not listed by source
- Location
- San Francisco
- Occupation
- Software Developers(O*NET 15-1252.00)
Hiring context
How this role compares at Gamma
Gamma has 31 live roles in MeritLog’s catalog across 8 job families, and 2 of them are in data & analytics. 0 of those listings publish a pay range, a disclosure rate of 0%.
Gamma concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd do
- Own and evolve our end-to-end event pipeline architecture, from Kafka ingestion through Snowflake analytics, setting technical direction for data infrastructure
- Design and architect distributed data systems that scale to orders of magnitude more data volume while maintaining world-class query performance
- Lead initiatives to build and optimize CDC (change data capture) pipelines and streaming data transformations at massive scale
- Establish best practices for data quality, pipeline reliability, and system observability across the organization
- Drive strategic technical decisions about data modeling, infrastructure architecture, and technology choices
- Mentor engineers and elevate data engineering practices across analytics, product, and engineering teams
What they're asking for
- 10+ years as a data or software engineer with deep expertise in distributed systems, data infrastructure, and high-growth SaaS products at massive scaleExperience
- Expert-level knowledge of Apache Kafka (producers, consumers, Kafka Connect, stream processing) and event streaming platformsSkill
- Extensive hands-on experience with Snowflake, including performance optimization, cost management, and data modeling; strong foundation in Postgres, CDC patterns, and replication strategiesSkill
- Proven track record architecting and leading major data infrastructure initiatives through orders-of-magnitude growthSkill
- Experience establishing best practices and driving technical strategy across organizationsSkill
- Strong communication skills with a history of influencing technical direction across engineering, analytics, and leadershipSkill
- Proficiency with dbt, Terraform, and working knowledge of data governance, privacy compliance (GDPR, CCPA), and security best practicesSkill
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
ABOUT THE ROLE You'll own Gamma's data infrastructure and architecture as we scale to hundreds of millions of users and petabytes of data. This means defining the technical strategy for our end-to-end event pipeline architecture, designing distributed systems that handle massive scale with reliability, and establishing the foundation for how data flows through Gamma. You'll solve the hardest data engineering challenges we face while setting the technical direction for data infrastructure across the company. As a Staff Data Engineer, you'll balance hands-on engineering with technical leadership. You'll architect solutions for orders of magnitude growth, mentor engineers across the organization, and drive strategic decisions about our data stack. You'll work closely with analytics, product, and engineering leadership to enable data-driven decision making at scale while building systems that serve millions of users and inform critical business decisions. Our team has a strong in-office culture and works in person 4–5 days per week in San Francisco. We love working together to stay creative and connected, with flexibility to work from home when focus matters most. WHAT YOU'LL DO - Own and evolve our end-to-end event pipeline architecture, from Kafka ingestion through Snowflake analytics, setting technical direction for data infrastructure - Design and architect distributed data systems that scale to orders of magnitude more data volume while maintaining world-class query performance - Lead initiatives to build and optimize CDC (change data capture) pipelines and streaming data transformations at massive scale - Establish best practices for data quality, pipeline reliability, and system observability across the organization - Drive strategic technical decisions about data modeling, infrastructure architecture, and technology choices - Mentor engineers and elevate data engineering practices across analytics, product, and engineering teams WHAT YOU'LL BRING - 10+ years as a data or software engineer with deep expertise in distributed systems, data infrastructure, and high-growth SaaS products at massive scale - Expert-level knowledge of Apache Kafka (producers, consumers, Kafka Connect, stream processing) and event streaming platforms - Extensive hands-on experience with Snowflake, including performance optimization, cost management, and data modeling; strong foundation in Postgres, CDC patterns, and replication strategies - Proven track record architecting and leading major data infrastructure initiatives through orders-of-magnitude growth - Experience establishing best practices and driving technical strategy across organizations - Strong communication skills with a history of influencing technical direction across engineering, analytics, and leadership - Proficiency with dbt, Terraform, and working knowledge of data governance, privacy compliance (GDPR, CCPA), and security best practices COMPENSATION RANGE: The base salary for this full-time position, which spans multiple internal levels depending on qualifications, ranges between $230K - $310K plus benefits & equity. Final offer amounts are determined by multiple factors, including but not limited to experience and expertise in the requirements listed above. If you're interested in this role but you don't meet every requirement, we encourage you to apply anyway! We're always excited about meeting great people.