Senior Data Analyst, Fabulous

• University Degree in Engineering, Computer Science or Applied Mathematics
• A minimum of 4 years of experience in Data or Analytics Engineering
• Excellent hands on skills in Data Modelling
• Excellent SQL skills (even better if this is coupled with dbt experience or a similar sql based data modelling tool)
• Excellent Engineering skills (testing, clean coding, peer-reviewing, CD/CI, git workflows, agile workflows, etc…)
• Previous experience working with modern data stack tools and cloud based data warehouse (BQ, Snowflake, Redshift,..)
• Sound business acumen to mange your own projects and your business stakeholders
• Self-Starter with the ability to work autonomously and own ones projects fully
• Excellent written and verbal communication skills (English)
• Comfortable in a remote work environment (we are a remote-first organisation)

Desirable

• You will be part of the data team. We work cross-functionally with other teams to bring data-driven impact to Fabulous and its business units like Product Growth, User Acquisition and Finance
• You will work on Data Modelling and Analytics Engineering project to improve, enrich and maintain our data models and Analytics pipelines. Those projects will be in close collaboration with the head of Data & Analytics as the main stakeholder. The objective of those project is to further enable data analytics and scientist from the team to bring more impact to the business use cases they are working on in collaboration with business teams
• You will be responsible for contributing effectively to our code base: building, testing, reviewing and maintaining solid analytics pipelines using SQL and dbt. Help managing TechDebt and improving engineering practices and the project's architecture are also important responsibilities for this role. Solid methodical testing is key here to strengthen Data Observability
• You are expected to gradually own some aspects of the team's responsibilities (some parts of the code base, , have a strong saying in how the analytics project's architecture should evolve, contribute to team's evolution and continuous growth, help data analysts and scientist improve and strengthen their engineering skills ...)
• You will be expected to speak up your mind and contribute proactively and effectively to improving the team's practices, cohesion, impact and mission
• You are expected to be highly autonomous and show a sense of ownership and ability to effectively manage your own projects and stakeholders. This should be fulfilled with minimum guidance from the Head of Data & Analytics
• You will help mentoring more junior members and sharing knowledge and practices within the team to level up everyone's skills
• You are expected to contribute effectively to our functional documentation in a way that is clear, concise and useful for future collaborators and readers
• Practically the team handles 3 data areas:
• Data Project Management: Based on business needs, we sort requests, refine requirements and ACs with business and/or data stakeholders, prioritise, plan and executive while communicating proactive and frequently to ensure visibility and a well connected feedback loop with involved parties (reviewer, data stakeholder, business stakeholder)
• Applied Analytics & Data Science: Data Exploration, Defining appropriate and agile analytics approach. We aim for simplicity and interpretability but don't shy away from complexity when faced with it
• All new projects have a strong Data Science component during the first MVP iteration
• Analytics Engineering: Data Modelling and Transformations to serve build, maintain and scale our Analytics Pipelines. Practically, as soon as an MVP gets validated by different stakeholders, we start implementing it and improving it iteratively in our dbt project
• Testing and data observability is a highly important component. Proper architecture that helps manage TechDebt is another key element here as well
• All members of the team are expected to excel in at least 2 of the above mentioned areas to be autonomously impactful
• We work in an agile manner by splitting bigger projects into iterations that rarely expand beyond 3 weeks to ensure impact
• We have a modern cloud-based Data-Stack (Fivetran - Big Query - dbt - Amplitude - Metabase - Looker Studio) and want to consolidate our ranks with a capable well-rounded Senior Analytics Engineer who can integrate our agile context smoothly and bring value quickly
• Team members enjoy in house challenges and Slack event and work across a variety of tools. Meetings are kept to a minimum and deep work is encouraged