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Data Analyst Roles at GitLab

The data analyst role at GitLab is a hybrid role: part data analyst, part data scientist, part data warehouse engineer, and part backend engineer.

This role will require an inquisitive and business-oriented mindset with the ability to implement rigorous database solutions and best practices in order to produce and influence the adoption of robust quality data insights to drive business decisions in all areas of GitLab.

Data Analyst

Job Grade

The Data Analyst is a grade 6.

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
• Provide data modeling expertise to all GitLab teams through code reviews, pairing, and training to help deliver optimal, DRY, and scalable database designs and queries in Snowflake and in Periscope
• Approve data model changes as a Data Team Reviewer and code owner for specific database and data model schemas
• Own the end-to-end process of on-call data triaging from reading Airflow logs, to diagnosing the data issue, and to verifying and implementing a solution with an automated alerting system (ChatOps, etc) as well as providing data support for all GitLab members
• 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
• This position reports to the Manager, Data

Requirements

• 2+ years experience in an analytics role
• Experience building reports and dashboards in a data visualization tool
• Passionate about data, analytics and automation. Experience cleaning and modeling large quantities of raw, disorganized data (we use dbt)
• Experience with a variety of data sources. Our data includes Salesforce, Zuora, Zendesk, Marketo, NetSuite, Snowplow and many others (see the data team page)
• Demonstrate capacity to clearly and concisely communicate complex business logic, technical requirements, and design recommendations through iterative solutions
• Deep understanding of SQL in analytical data warehouses (we use Snowflake SQL) and in business intelligence tools (we use Periscope)
• Hands on experience working with SQL, Python, API calls, and JSON, to generate business insights and drive better organizational decision making
• Familiarity with Git and the command line
• Deep understanding of relational and non-relational databases, SQL and query optimization techniques, and demonstrated ability to both diagnose and prevent performance problems
• Effective communication and collaboration skills, including clear status updates
• Positive and solution-oriented mindset
• Comfort working in a highly agile, intensely iterative environment
• Self-motivated and self-managing, with strong organizational skills
• Ability to thrive in a fully remote organization
• Share and work in accordance with our values
• Successful completion of a background check
• Ability to use GitLab

How you'll ramp

By the 30 day mark…

• Helping definition-related conversations, acting as thought-leader to functional group
• Working in Periscope, writing SQL queries to produce dashboards
• Clear sense of where prioritization comes from
• Understands values/working the GitLab way
• Comfortable participating in triage rotation without a named backup

By the 60 day…

• Comfortable working with dbt via the command line
• Comfortable participating in triage rotation on own
• Contributing to internal conversations on data organization and structure

By the 90 day…

• Comfortable building a new data source from scratch
• Can show team members answers in the handbook/in Periscope
• Regularly contributing to documentation and housekeeping improvements for the team
• Can gather requirements, scope, and build analysis with little-to-no guidance from more senior members of the team

Levels

Read more about levels at GitLab.

Intern

An intern is not required to meet the standards of an intermediate data analyst but she or he is required to be interested in developing in towards them. An intern must:

• Have a track record of asking hard questions and thinking critically
• Self-starter committed to remote work and its intricacies
• Proactive, positive, energetic, customer service personality
• Ability to articulate in a clear, concise manner, disseminating complete and accurate information
• Ability to deal effectively with people of multi-cultural societies
• Attention to detail
• Organizational skills

What you'll do

• Work with and learn from a talented team of data professionals
• Develop and execute a project with the help of a mentor
• Write blog posts about your learnings
• Update, maintain, and coordinate meetings
• Update the handbook using git and GitLab
• Identify Data team process weaknesses and blindspots
• Contribute fresh perspective and speak up where you can add value

Junior Data Analyst

Job Grade

The Junior Data Analyst is a grade 5.

Requirements

Junior Data Analysts share the same requirements and responsibilities outlined above, but typically join with less or alternate experience than a typical Data Analyst.

Senior Data Analyst

Job Grade

The Senior Data Analyst is a grade 7.

Requirements

All of the responsibilities of a Data Analyst, plus:

• 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

Specialties

Engineering

• Support all departments in the engineering division by helping drive the standardization, capture, automation, and implementation of performance indicators
• Be intimately familiar with productivity metrics
• Priorities will be set by the VP, Engineering but will collaborate with and reporting into the Data Team

Finance

• Support the FP&A team in driving financial and operational initiatives by analyzing data and discovering insights
• Focus on financial and operational specific data
• Priorities will be set by the Manager, Financial Planning and Analysis but will collaborate with and report into the Data Team
• Spend 80% of time supporting the FP&A team and spend the remaining 20% of time contributing to the Data Team
• The Manager, Financial Planning and Analysis will evaluate the analyst on 80% of the goals in the experience factor worksheet relating to supporting FP&A and the Manager, Data will evaluate the analyst on the remaining 20% of goals relating to supporting the Data Team

Growth

• Support the product management function in driving product growth, reducing churn, increasing user engagement by analyzing data and discovering insights
• Focus on product-specific data - usage ping, SaaS DB, Snowplow events
• Priorities will be set by a Product Manager, Growth but will collaborate with and report into the Data Team

Product

• Support the Product function by spearheading tracking and reporting initiatives
• Focus on product usage metrics across SaaS and self-managed products
• Build cross-functional analysis to drive strategic decision-making
• Priorities will be set by a Director of Product but will collaborate with and report into the Data Team

Sales

• Coordinate with SalesOps to improve and automate tracking potentially insightful data points
• Focus on cross-functional analysis that can help drive sales conversations (e.g. product usage into renewal conversations)
• Priorities will be set by the sales function but will collaborate with and report into the Data Team

Marketing

• Coordinate with Marketing to improve and automate tracking potentially insightful data points and by analyzing data and discovering insights
• Support Marketing by helping drive the standardization, capture, automation, and implementation of performance indicators
• Assist with budgeting, planning, and strategy
• Focus on cross-functional analysis that can help drive marketing conversations
• Report to Director of Marketing Operations working closely with the Chief Marketing Officer
• Member of the Marketing Operations Team

People

• Coordinate and support the People function by automating all reports from Greenhouse, BambooHR, and Google Sheets into reporting dashboards.
• Focus on cross-functional analysis to help other departments identify opportunities for improvement within their recruiting, hiring, and retention policies.
• Priorities will be set by Director, People Operations but will collaborate with and report into the Data Team

Performance Indicators (PI)

• Adoption of Data Team BI charts throughout company
• % of issues requested triaged with first response within 36 hours (per business unit)

Hiring Process

• Candidates for this position can expect the hiring process to follow the order below. Please keep in mind that candidates can be declined from the position at any stage of the process. To learn more about someone who may be conducting the interview, find their job title on our team page.

• Selected candidates will be invited to fill out a short questionnaire.
• Next, candidates will be invited to schedule a screening call with our Global Recruiters
• Next, candidates will be invited to schedule a first interview with our Manager, Data (Analytics)
• Next, candidates will be invited to schedule a second interview with the business division DRI
• Next, candidates will be invited to schedule a third interview with one a member from our Data team
• Next, candidates will be invited to schedule a fourth interview with a specialty Engineering manager

Additional details about our process can be found on our hiring page.

Career Ladder

The next step in the Data Analyst job family is to move to the Data Management job family.

Compensation

Components

Compensation at GitLab consists of:

1. Base salary
2. Options
3. Bonus in some functions
4. Discretionary bonus
5. Retirement contributions in eligible regions
6. Pay for equipment
7. Remote work
8. Unlimited time off
9. Benefits

About GitLab

GitLab Inc. is a company based on the GitLab open-source project. GitLab is a community project to which over 2,200 people worldwide have contributed. We are an active participant in this community, trying to serve its needs and lead by example. We have one vision: everyone can contribute to all digital content, and our mission is to change all creative work from read-only to read-write so that everyone can contribute.

We value results, transparency, sharing, freedom, efficiency, self-learning, frugality, collaboration, directness, kindness, diversity, inclusion and belonging, boring solutions, and quirkiness. If these values match your personality, work ethic, and personal goals, we encourage you to visit our primer to learn more. Open source is our culture, our way of life, our story, and what makes us truly unique.

Top 10 reasons to work for GitLab:

1. Work with helpful, kind, motivated, and talented people.
2. Work remote so you have no commute and are free to travel and move.
3. Have flexible work hours so you are there for other people and free to plan the day how you like.
4. Everyone works remote, but you don't feel remote. We don't have a head office, so you're not in a satellite office.
5. Work on open source software so you can interact with a large community and can show your work.
6. Work on a product you use every day: we drink our own wine.
7. Work on a product used by lots of people that care about what you do.
8. As a company we contribute more than we take, most of our work is released as the open source GitLab CE.
9. Focused on results, not on long hours, so that you can have a life and don't burn out.
10. Open internal processes: know what you're getting in to and be assured we're thoughtful and effective.

See our culture page for more!

Work remotely from anywhere in the world. Curious to see what that looks like? Check out our remote manifesto and guides.