Data Engineer

• Bachelor's Degree in Computer Science, Information Systems, or a related field. A master's degree specializing in AI, machine learning, or Finance is highly preferred.
• Proven experience as an AI Data Engineer or in a similar role, with a focus on financial services and AI applications.
• Strong programming skills in languages such as Python, Java, or Scala, with experience in developing AI and machine learning models in the financial domain.
• In-depth knowledge of AI and machine learning concepts and techniques, particularly as applied to financial services, including risk modeling, fraud detection, algorithmic trading, or credit scoring.
• Experience with AI frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn) and familiarity with AI model development workflows within financial services.
• Proficiency in SQL and hands-on experience with relational databases (e.g., MySQL, PostgreSQL), specifically in the context of financial data management and analytics.
• Familiarity with cloud platforms such as AWS, Azure, or GCP, and experience with related AI and machine learning services (e.g., AWS SageMaker, Azure ML) within the financial industry.
• Strong understanding of data modeling and data warehousing concepts, with a focus on financial data structures and reporting requirements.
• Experience with big data technologies (e.g., Hadoop, Spark) and NoSQL databases (e.g., MongoDB, Cassandra) is a plus.
• Excellent analytical and problem-solving skills, with the ability to work with large and complex financial datasets.
• Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.
• Attention to detail and a commitment to delivering high-quality.

• Bachelor's Degree in Computer Science, Information Systems, or a related field. A master's degree specializing in AI, machine learning, or Finance is highly preferred.
• Proven experience as an AI Data Engineer or in a similar role, with a focus on financial services and AI applications.
• Strong programming skills in languages such as Python, Java, or Scala, with experience in developing AI and machine learning models in the financial domain.
• In-depth knowledge of AI and machine learning concepts and techniques, particularly as applied to financial services, including risk modeling, fraud detection, algorithmic trading, or credit scoring.
• Experience with AI frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn) and familiarity with AI model development workflows within financial services.
• Proficiency in SQL and hands-on experience with relational databases (e.g., MySQL, PostgreSQL), specifically in the context of financial data management and analytics.
• Familiarity with cloud platforms such as AWS, Azure, or GCP, and experience with related AI and machine learning services (e.g., AWS SageMaker, Azure ML) within the financial industry.
• Strong understanding of data modeling and data warehousing concepts, with a focus on financial data structures and reporting requirements.
• Experience with big data technologies (e.g., Hadoop, Spark) and NoSQL databases (e.g., MongoDB, Cassandra) is a plus.
• Excellent analytical and problem-solving skills, with the ability to work with large and complex financial datasets.
• Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.
• Attention to detail and a commitment to delivering high-quality.

Details

• The ideal candidate will have a self-starter attitude with high ownership, self-motivation, and accountability.

Duties

• Design, develop, and maintain our data infrastructure, including data pipelines, databases, data warehouses, and data lakes, with a specific focus on financial services applications.
• Collaborate closely with data scientists, financial analysts, and other stakeholders to gather and analyze data requirements for AI projects, ensuring the availability, quality, and relevance of financial data.
• Implement scalable and efficient data processing and integration solutions to support AI model development and deployment, specifically tailored for financial services use cases.
• Build and optimize AI data models, ensuring data integrity, accuracy, and compliance with regulatory requirements and financial industry standards.
• Work closely with cross-functional teams to identify and integrate external financial data sources, such as market data feeds, transaction data, and economic indicators, for training and enhancing AI models.
• Implement data governance practices and adhere to data security and privacy regulations, particularly within the financial services context.
• Troubleshoot and resolve AI data-related issues, such as performance bottlenecks, data quality problems, and model performance discrepancies, with a strong focus on financial domain challenges.
• Monitor and maintain AI data systems to ensure their availability, reliability, and performance, and proactively implement necessary optimizations and improvements.
• Stay up to date with the latest trends and advancements in AI, machine learning, and financial technologies, and apply them to continuously improve our AI data infrastructure and processes.