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Location – Toronto, ON (Mandate 4 days onsite)
Note – In-person interview is mandatory
We are seeking an experienced Python Developer with a strong background in the banking sector to join our team. The ideal candidate will bring 5 years of hands-on Python development experience combined with expertise in data processing, feature engineering, and data cleansing. This role is critical in building and maintaining robust data pipelines and analytical solutions that support our banking operations and decision-making processes.
Key Responsibilities
Data Engineering & Processing
Design, develop, and maintain scalable Python-based data processing pipelines to handle large volumes of banking data including transactions, customer information, and financial records
Implement efficient ETL (Extract, Transform, Load) processes to integrate data from multiple banking systems and external sources
Optimize data workflows for performance, reliability, and scalability to support real-time and batch processing requirements
Feature Engineering & Model Support
Collaborate with data scientists and analysts to develop sophisticated feature engineering solutions for credit risk models, fraud detection systems, and customer analytics
Transform raw banking data into meaningful features that enhance predictive model performance
Create and maintain feature stores and reusable feature pipelines to support machine learning initiatives
Data Quality & Cleansing
Implement comprehensive data validation frameworks and cleansing routines to ensure data accuracy, consistency, and completeness
Identify and resolve data quality issues including missing values, outliers, duplicates, and inconsistencies in banking datasets
Develop automated data quality monitoring and alerting systems to proactively detect and address data anomalies
Technical Development
Write clean, efficient, and well-documented Python code following industry best practices and internal coding standards
Develop and maintain APIs and microservices to support data access and integration across the organization
Implement unit tests, integration tests, and participate in code reviews to ensure code quality and reliability
Required Qualifications
Experience & Education
Bachelor's degree in Computer Science, Software Engineering, Data Science, or related field (or equivalent practical experience)
Minimum 6 years of professional experience in Python development
Proven experience working in the banking, financial services, or fintech industry with understanding of banking products, processes, and regulatory requirements
Technical Skills
Expert-level proficiency in Python and its data ecosystem including pandas, NumPy
Strong experience with data processing.
Hands-on experience with feature engineering techniques including encoding, scaling, binning, transformation, and dimensionality reduction
Demonstrated expertise in data cleansing methodologies including handling missing data, outlier detection, data normalization, and validation
Proficiency with SQL and experience working with relational databases (PostgreSQL, MySQL, Oracle)
Experience with version control systems (Git) and CI/CD pipelines
Domain Knowledge
Strong understanding of banking domain concepts including retail banking, corporate banking, payments, lending, or risk management
Knowledge of banking data structures, transaction processing, and regulatory reporting requirements
Awareness of data privacy regulations and compliance standards relevant to banking (e.g., GDPR, PCI-DSS, Basel III)
Preferred Qualifications
Experience with machine learning model deployment and MLOps practices
Familiarity with data visualization tools such as Tableau, Power BI, or Python libraries (matplotlib, seaborn, plotly)
Understanding of software design patterns and architectural principles
Exposure to Agile/Scrum development methodologies
Sprachkenntnisse
- English
Hinweis für Nutzer
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