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Mid-level, generalist Data Engineer role without strong employer brand increases applicant competition moderately.
Data engineering skills are moderately transferable across industries but favor enterprise data platform experience.
Explicit 3–6 years requirement plus mandatory data engineering stack increases shortlisting strictness.
Develop and maintain enterprise data pipelines and curated datasets supporting analytics, automation, and GenAI across multiple enterprise domains.
Ensure data products follow Data-as-a-Product principles with metadata, lineage, quality controls, and governance for consistent self-service use.
Collaborate with cross-functional teams (Product Managers, Data Engineers, Data Scientists) to deliver AI-ready, optimized data assets for advanced analytics and machine learning.
3 to 6 years of relevant data engineering experience.
Bachelor's degree in Computer Science, IT, Engineering, Data Analytics or equivalent practical experience.
Experience with ETL/ELT, data integration, SQL, data modeling, metadata management, and enterprise-scale data governance.
Experience working in Agile, cross-functional teams collaborating with product, engineering, and business stakeholders.
Experienced in developing scalable data solutions with strong emphasis on Data-as-a-Product operating models including data catalogs and lineage.
Familiar with modern big data and cloud technologies like Spark, Kafka, Hive, and cloud-based data processing platforms.
Comfortable translating complex business/product needs into efficient, maintainable technical data architectures optimized for AI/ML and GenAI use cases.