Analytics Engineer

**The Data team** The Data team’s main objectives are to serve our various teams in order to free up their work & make them more efficient and productive, but also to work on long-term AI projects to unlock growth levers for the company. This includes: - Providing business teams with key data and indicators to monitor their activity and their customers’ health, and automate certain tasks to allow them to focus on higher added value ones. - Building AI algorithms to take our products to the next level: price and transit time predictions, automated document parsing, scoring and attrition models, etc. - Doing data engineering to make sure that our databases are up and running, and that our different processes are monitored and raise alerts when a problem arises. We're looking for someone to lead the data pole at Ovrsea, and keep the data flame burning across all teams. **Challenges of this position**: - Improve the current stack. - Keep the data culture going. - Encourage data consumption by providing clear, easy-to-use and easy-to-discover tables. - Unlock new use cases of data science algorithms, and GPT (we have worked on a few projects, but this is just the beginning). - Maintain a clean business intelligence pipeline, ensure we don’t build up debt, enforce best practices - Enrich 3rd party tools with reports and data we push to them. **What is it like to work in Data at OVRSEA?** The ambition of data team members at Ovrsea is to be Analytics Engineer: we try to be as full stack as possible to ensure redundancy in terms of knowledge, and to each have the 360 vision of the data stack. It also allows to make enlightened choices, because we understand the contraints and the consequences or our choices and designs. At OVRSEA, we believe that the analytical workforce is within the business teams, and to make sure this works, we do everything to promote self-BI. - Everybody at OVRSEA uses Metabase (our BI tool), and every user can run analyses through it thanks to a very intuitive interface and the refined data we provide them. - The data team provides **ready-to-use tables** to the teams. One of the missions of the data analyst is to identify the needs of the teams in terms of BI, and create the adapted datasets to let the business teams run analyses autonomously. Thanks to the self BI, 80% of the data needs from the business teams are covered. The remaining 20% is the part that is too complex to be handled through Metabase’s interface, and is tackled by the data team. A complete and efficient self BI system allows us to greatly reduce the support part and only keep the most complex and intellectually challenging support requests. **Your missions** Reporting directly to the CTO, you are going to take over the Ovrsea data division. **Data Analytics & Models creation (40%)** You will work on advanced analytics for the different business teams. We use OKRs and KPIs to prioritize the different topics each quarter, and the data team works closely with one Data Champion from each team, to make sure that they have actionnable data, and proactively identify data needs. Among the key topics are the internationalization of our operations, allocations (negotiated space), our quality of service... The idea behind Self BI is that any user can run analyses. For that, they need to rely on pre-processed tables. To that end, you will play a major part in: - Creating the pre-processed tables : we mostly use SQL (with dbt) - Making sure that they are documented and tested - Enforcing the single source of truth both in our code and in people’s minds - Proactively identifying the tables you could create to make the business teams more autonomous in their analyses and their data consumption - Contributing to keeping Metabase, our BI tool, structured, organized and insightful **Data Engineering (30%)** In terms of data engineering, the topics are numerous: - We need to gather data from different sources and you will write some scripts / use the right tool to ingest data into our database - You will ensure that our processes run smoothly : for this, we need to improve observability, monitoring and alerting - You will take initiatives to improve the existing stack : we have weekly meetings dedicated to optimizing what we have, and you will lead some of these meetings **Data project management (20%)** The number 1 KPI in the data team is usage. You will need to make sure that the things you work on are used. For this, there is a big part of project management: - challenging the need - designing the data solution - accompanying the teams in their usage of data - gathering feedback and following performance of the different projects we are working on **Data Science (10%)** You will also work on some data science algorithms, but the reality of data science, is that it is 90% project management, design reflection, engineering, and