Marketing Data Analytics Engineer (CA)
The Marketing Data Analytics Engineer will build and maintain data infrastructure for marketing analytics, automating reporting workflows and enhancing data quality across platforms.
Coretek provides AI, cloud, security, and managed services. We are seeking a Data Consultant Co-Op to help build the pipelines and datasets that support reporting, business operations, and AI solutions.
Coretekβs AI & Data Co-Op Program places you on a team supporting internal initiatives and client projects. With guidance from experienced Consultants, you will connect data sources, prepare datasets, and test your work. You will own defined tasks and incorporate feedback before changes go through review and approval.
You will gain practical experience with SQL, Python, and Microsoft data platforms such as Azure and Microsoft Fabric. Depending on project needs, you may also work with Power BI, Elastic, and Tines.
Responsibilities
Requirements
Requirements
Successful co-ops produce data that others can trust and use. They check results against the source, follow through on commitments, and leave clear documentation. Progress is reflected in reliable data jobs, fewer recurring errors, and work the team can maintain after the co-op ends.
The Marketing Data Analytics Engineer will build and maintain data infrastructure for marketing analytics, automating reporting workflows and enhancing data quality across platforms.
Build and enhance a high-volume data lake by developing reliable pipelines and data assets, optimizing distributed workloads, and supporting identity security research and analytics.
Build and improve data pipelines and analytics processing on a modern data platform, contributing enterprise-grade code across the full development lifecycle from design and scoping through implementation, review, and deployment.
Build and maintain data pipelines, integrate partner data, and transform raw information into reliable datasets while learning how scalable data infrastructure is designed and operated.
Build and maintain data pipelines and scalable infrastructure for analytics and machine learning, integrating data from multiple sources while supporting data quality, governance, security, and performance.