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Metro location, generalist data engineering role with common Python/cloud skills increases candidate competition.
Core data engineering skills are highly transferable across industries, so background fit sensitivity is low.
Explicit 1-2 years requirement plus mandatory Python and pipeline experience enforce moderate filters.
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Architect, design, implement, and optimize data ingestion, transformation, and distribution pipelines and processes.
Develop data models, processing pipelines, and back-end services supporting data science teams, including automation and integration.
Build analytics capabilities to support financial services AI applications.
1-2 years relevant experience in Data Engineering, ETL, and Automation.
University degree in Mathematics, Computer Science, Engineering, or similar.
Proficient in writing clean, scalable Python code.
Experience building scalable data pipelines for structured and unstructured data.
Experience working in cross-functional teams in fast-paced, evolving environments with changing priorities.
Familiarity with deploying ML solutions on at least one major cloud ML stack (Azure, AWS, or GCP) and Kubernetes clusters.
Strong analytical, problem-solving, and quantitative skills with professional communication abilities in English.