Sr Engineer, Data Prsnliztn

Tractor Supply Company•Brentwood, TN

18d•Onsite

About The Position

The Senior Data Engineer for Data Personalization and Analytics is pivotal in executing our customer data personalization strategy using advanced analytics and engineering techniques. Specializing in SQL, Python, Adobe suite, Alteryx, and Azure, this role is at the heart of our data engineering efforts to model insights and support targeted campaigns. The Senior Data Engineer will also take a leading role in managing our digital analytics across various channels, ensuring data-driven decisions that enhance customer satisfaction and business growth.

Requirements

5+ years of experience in a data engineering role, with a strong background in SQL, Python, and data pipeline development.
Bachelor’s degree in Computer Science, Engineering, or related field.
Expertise in Python programming , SQL, Alteryx and Tableau.
Experience with personalization suits like Adobe Campaign, Adobe Target, AEM.
Experience with cloud-based analytical tools (MS Azure, Snowflake, or similar).

Nice To Haves

Direct experience with large-volume customer data sets.
Experience connecting multiple disparate data sources in the course of analysis.
Strong analytical and problem-solving skills, with the ability to translate business objectives into analytical projects and solutions.

Responsibilities

Develop and maintain scalable data/deployment pipelines, integrating various data sources using SQL, Python, and Alteryx, ensuring high data quality and accessibility.
Work closely with analytics and business teams to assist with data-related technical issues and support data infrastructure needs.
Work with stakeholders to assist with data-related technical issues and support their data infrastructure needs.
Manage the deployment of analytics solutions on the Adobe platform.
Collaborate with cross-functional teams to align data personalization efforts, reporting measurable outcomes from digital and social campaigns.
Contribute to the management of the budget and resources dedicated to data personalization projects.
Foster an environment of continuous learning and development within the data analytics and personalization team.