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Mid-level, generalist Data Engineer role with common 3-6 year band and broad skillset attracts strong competition.
Core data engineering skills are transferable across industries despite domain-specific datasets and tools.
Explicit 3-6 years and concrete data engineering skill requirements enforce moderate shortlisting strictness.
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Develop and maintain enterprise data products by building data pipelines, transformations, and curated datasets supporting reporting, analytics, automation, and GenAI use cases.
Apply Data-as-a-Product principles to create reusable, discoverable, and governed data assets with metadata, lineage, and quality controls for self-service consumption.
Collaborate with Product Managers, Data Engineers, Data Scientists, and Solution Engineers to deliver AI-ready data products optimized for analytics and machine learning applications.
3 to 6 years of relevant experience in data engineering or related roles.
Bachelor's degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.
Experience developing and supporting ETL/ELT processes, data pipelines, and data transformations using modern data platforms and cloud technologies.
Working knowledge of data modeling, SQL, data quality practices, metadata management, and data governance principles at enterprise scale.
Experienced in Agile, cross-functional team environments collaborating with product, engineering, analytics, and business stakeholders.
Skilled in translating business and product requirements into scalable, maintainable, and reusable technical solutions.
Preferably familiar with Data-as-a-Product operating models and exposure to AI/ML or GenAI initiatives, including preparation of AI-ready datasets and semantic models.