.Fender Musical Instruments Corporation is a world-famous brand with offices across the globe.
Fender was born in Southern California and has built a worldwide influence beyond the studio and the stage.
A Fender is more than an instrument; it's a cultural symbol that resonates globally.We are searching for an Analytics Engineerto join our fully remote, dynamic Data and Analytics team in Mexico.
Where you'll play a pivotal role in transforming FMIC into a data-driven powerhouse!
We aim to equip the company with scalable, sustainable data products while fostering a culture where every decision is informed by data.
As an Analytics Engineer, you'll help build impactful solutions that empower stakeholders to extract insights and drive strategic growth.Essential Functions:Develop and maintain robust documentation and best practices while enforcing data quality standards and governance processesCollaborate with cross-functional teams to design data assets, integrate analytics solutions, and ensure scalability and maintainabilityConduct technical research and data profiling to recommend innovative, value-driven solutionsQuery and assemble large, complex datasets from diverse data sources to meet business requirementsProactively identify and resolve data management issues to enhance quality, compliance, and scalabilityPromote data best practices and provide education to foster a data-driven culture across the organizationAdditional duties as assignedQualifications:BA/BS degree in Industrial or Systems Engineering, Computer Science, Analytics, Statistics, Informatics, Information Systems or another quantitative field1 to 3 years of experience in root cause analysis of data and processes to solve business challenges and identify improvement opportunitiesAbility to work independently as a Data SME (Subject Matter Expert)Strong communication skills to explain technical issues, convey requirements, and address questions for non-technical stakeholdersProficiency in SQL and PythonKnowledge of data modeling concepts, including relational and dimensional models, keys, constraints, and best practices for normalization and indexingAdvanced proficiency in spoken and written English