You will help shape the future of the Fivetran data pipeline platform for businesses large and small. With your brilliant data insights, you will enable us to measure the success of our product with respect to its robustness and effectiveness. Your quick ad hoc analyses for your engineering team compatriots will identify metrics to lift and targets to hit. Your contributions will help us continue to deliver a product that provides analysts worldwide with reliable access to the data they need, without having to ask an engineer to get it for them.
- Partner with Engineering stakeholders to quantitatively understand problems and potential solutions; analyze disparate data sets in creative ways to generate new insights
- Develop innovative metrics for measuring engineering performance; set benchmarks and provide the means to monitor progress towards targets
- Areas of focus will include: team performance and productivity (sprint efficiency, workload estimation accuracy); product reliability (uptime, bugs); and process analysis (SLA compliance, Support to Engineering handoff efficiency)
- Collaborate with cross-functional partners to tackle challenges where engineering data and broader business data intersect
- Maintain and enhance analytics infrastructure at Fivetran in cooperation with analysts across the organization
- Assist in training new hires, colleagues, partners and our fan club on the workings of Fivetran’s data and what it means to be a data-driven culture
- Experience using analytics to support key business decisions, preferably at a high-growth, SaaS or B2B product company
- Demonstrated experience using a variety of tools including SQL, Looker, Tableau, etc. to analyze and model raw data sets; experience with R or Python a plus
- Resourceful, self-motivated and able to successfully complete multiple, competing projects under tight deadlines
- Strong prioritization & organizational skills
- Excellent verbal & written communication skills; ability to effectively collaborate with a talented team of engineers and product managers.
- Bachelor’s in a quantitative discipline such as statistics, applied mathematics, economics, computer science, engineering, or a related field; or equivalent analytical acumen/analytics experience