Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Mid-level data engineer with broad skillset and common technologies increases competition density.
Core data engineering skills are transferable, though enterprise data-product and governance experience increases domain specificity.
Moderate due to required domain skills and tooling but no explicit years or mandatory certifications.
Develop and maintain enterprise data products by building scalable data pipelines, transformations, and curated datasets supporting reporting, analytics, automation, and GenAI use cases across multiple business domains.
Implement Data-as-a-Product principles to create reusable, discoverable, and governed data assets with metadata, lineage, and quality controls enabling self-service consumption.
Collaborate with Product Managers, Data Engineers, Data Scientists, and Solution Engineers to ensure delivery of AI-ready data products that are reliable and optimized for analytics and machine learning.
Experience developing and supporting data integration, ETL/ELT processes, data pipelines using modern data platforms and cloud technologies.
Working knowledge of data modeling, SQL, data quality, metadata management, and governance principles for enterprise-scale data products.
Bachelor's degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.
Work Experience Required: Not explicitly mentioned in the JD.
Proven ability to translate business and product requirements into scalable, maintainable technical data solutions.
Experience working in Agile, cross-functional teams collaborating with product, engineering, analytics, and business stakeholders.
Exposure to Data-as-a-Product models and AI/ML or GenAI initiatives preparing AI-ready datasets and supporting intelligent business solutions.