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Known financial-services employer, popular data-engineer role, and Python/ETL skillset attract moderate applicant competition.
Requires core data-engineering skills (Python, ETL, Airflow, AWS), making industry transferability limited.
Mandates 6+ years plus specific Python, ETL, Airflow/Glue, AWS, and data warehousing expertise.
Build and maintain scalable, production-grade Python-based data pipelines and ETL frameworks to support business intelligence and AI/ML models.
Design, develop, optimize, and monitor robust data processing solutions across cloud platforms ensuring data quality, consistency, and reliability.
Collaborate with data scientists, analysts, and stakeholders to translate data requirements into technical solutions and support critical AI models and existing Python scripts.
Minimum 2 years of work experience in data engineering; 3+ years preferred.
Strong Python development skills with experience in data processing libraries (Pandas, NumPy) and Python-based ETL frameworks (e.g., Apache Airflow, AWS Glue).
Hands-on experience with cloud platforms, specifically AWS and related cloud-native data services.
Degree from a university preferred.
6+ years of hands-on experience in data engineering with expert-level Python skills focused on scalable, cloud-based data pipeline development.
Familiarity with data warehousing concepts, data modeling, SQL/NoSQL databases, and machine learning workflow requirements for ML-ready datasets.
Experience working directly with data scientists, analysts, and business teams to produce production-ready datasets and maintain critical AI models and automation standards.
