About the job

We are looking to add a Senior Data Analyst to our team here at GitLab!

The mission of the Data Analytics team is to maximize the impact of business decisions and strategy with data solutions that are trusted and scalable.

We do this by helping all GitLab teams move up the Data Value Pyramid by means of our GitLab values and our Data Team Principles .

Want to learn more about the Data Team? Watch this video to learn more

Responsibilities

• Collaborate with other functions across the company by building reports and dashboards with useful analysis and data insights
• Explain trends across data sources, potential opportunities for growth or improvement, and data caveats for descriptive, diagnostic, predictive (including forecasting), and prescriptive data analysis
• Deep understanding of how data is created and transformed through GitLab products and services provided by third-parties to help drive product designs or service usage or note impacts to data reporting capabilities
• Understand and document the full lifecycle of data and our common data framework so that all data can be integrated, modeled for easy analysis, and analyzed for data insights
• Document every action in either issue/MR templates, the handbook , or READMEs so your learnings turn into repeatable actions and then into automation following the GitLab tradition of handbook first!
• Expand our database with clean data (ready for analysis) by implementing data quality tests while continuously reviewing, optimizing, and refactoring existing data models
• Craft code that meets our internal standards for style, maintainability, and best practices for a high-scale database environment. Maintain and advocate for these standards through code review
• Contribute to and implement data warehouse and data modeling best practices, keeping reliability, performance, scalability, security, automation, and version control in mind
• Follow and improve our processes and workflows for maintaining high quality data and reporting while implementing the DataOps philosophy in everything you do

Senior Data Analyst Requirements

• Extends that of the Data Analyst (Intermediate) requirements
• 5 years experience of SQL in analytical warehouses (we use Snowflake)
• 5 years experience with Business Intelligence / Data Visualization tools (we use Periscope)
• Experience using Product Analytics Platforms, like Amplitude or Mixpanel, Optimizely, PostHog etc.
• Advocate for improvements to data quality, security, and query performance that have particular impact across your team as a Subject Matter Expert (SME)
• Solve technical problems of high scope and complexity
• Exert influence on the long-range goals of your team
• Understand the code base extremely well in order to conduct new data innovation and to spot inconsistencies and edge cases
• Experience with performance and optimization problems, particularly at large scale, and a demonstrated ability to both diagnose and prevent these problems
• Help to define and improve our internal standards for style, maintainability, and best practices for a high-scale web environment; Maintain and advocate for these standards through code review
• Represent GitLab and its values in public communication around broader initiatives, specific projects, and community contributions
• Provide mentorship for Junior and Intermediate Engineers on your team to help them grow in their technical responsibilities
• Deliver and explain data analytics methodologies and improvements with minimal guidance and support from other team members. Collaborate with the team on larger projects
• Build close relationships with other functional teams to truly democratize data understanding and access
• Influence and implement our service level framework SLOs and SLAs for our data sources and data services
• Identifies changes for the product architecture and from third-party services from the reliability, performance and availability perspective with a data driven approach focused on relational databases, knowledge of another data storages is a plus
• Proactively work on the efficiency and capacity planning to set clear requirements and reduce the system resources usage to make compute queries cheaper
• Participate in Data Quality Process or other data auditing activities