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Strong employer brand, mid-level generalist title, and common data engineering skillset increase candidate competition.
Core data engineering skills are broadly transferable, though finance domain knowledge increases role-specific fit.
Explicit 2–4 years requirement with mandatory data engineering skills (SQL, Python, Airflow) enforces strict filters.
Build and maintain data pipelines, transformations, and curated datasets to support analytics and machine learning needs.
Assist in MLOps workflows, model data preparation, monitoring, and contribute to CI/CD processes for analytics solutions.
Collaborate closely with engineers, data scientists, and analysts to deliver trusted, scalable analytics products with strong data governance and quality checks.
2–4 years of experience in data or analytics engineering.
Bachelor’s degree in computer science, engineering, or related discipline.
Working knowledge of SQL, Python, ETL/ELT concepts, and exposure to AWS cloud services.
Familiarity with data quality/validation, basic DevOps/CI/CD principles, and orchestration tools like Apache Airflow.
Experienced in building and maintaining scalable data pipelines and feature engineering pipelines in enterprise environments.
Comfortable working in a collaborative setting with data scientists and software engineers, focused on automation and AI-assisted development.
Eager to expand knowledge in MLOps and analytics platform technologies, and actively leverage AI tools to improve engineering productivity.