BI Data Engineer: ETL Pipelines & AI-Driven Analytics

TaylorMade Golf Company

Carlsbad (CA)

On-site

USD 100,000 - 125,000

Full time

14 days+

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Benefits offered by this job

Health benefits

Performance bonus

Product discounts

Holidays

Paid time off

Job summary

TaylorMade Golf Company is seeking a BI Data Engineer / Technical Engineer to join the IT-driven Business Intelligence team. You will build and maintain data pipelines, ETL/ELT processes, and governance practices powering TaylorMade's BI environment.

Work with analysts and application owners to design scalable data architectures, leverage AI tools to accelerate development, and ensure data quality. A Bachelor's and 2–4 years in data engineering are expected, with experience in BI tools and cloud

Qualifications

Bachelor’s degree in information technology, computer science, data analytics, or related field.
2–4 years of work experience in data engineering, BI development, or a closely related technical analytics role.
Demonstrated experience with ETL processes, data integration, and/or data warehouse environments.
Experience with BI tools such as Domo, Tableau, Power BI, or Business Objects.
Agile project management experience beneficial.
Data modeling knowledge and familiarity with data warehouse best practices a plus.
Data science, statistics, R, and/or Python programming background a plus.

Responsibilities

Design, develop, and maintain ETL/ELT processes and data pipelines supporting BI reporting and analytics.
Partner on data integration projects connecting source systems to the data warehouse and BI environment.
Support governance standards for BI operations, including monitoring, maintenance, and performance optimization.
Identify gaps or opportunities in governance policies and propose improvements.
Contribute to expansion of TaylorMade's BI data architecture with the Lead BI Engineer.
Work with application owners to ensure BI outcomes in enhancements and modifications.
Collaborate with senior team members on various BI projects.
Leverage AI-driven tools to accelerate ETL development and improve data reliability.
Apply established data architecture standards to new integrations and flag risks for review.