VP, Data Science Engineer, Group Asset Management
7 days ago
Job Responsibilities
- Develop and maintain infrastructure for enterprise data platforms and machine learning.
Data Platform Development & Management:
- Design, develop, and maintain data platforms that support large-scale data ingestion, storage, and processing using cloud-based data infrastructure.
- Implement and manage data warehousing and centralized data solutions tailored for asset management.
- Ensure data platform scalability, reliability, and security in accordance with financial regulators and policies.
- Evaluate and integrate new data technologies and tools to enhance data platform capabilities.
- Data Pipeline Development:Build and maintain robust and efficient data pipelines for data ingestion, processing, and transformation.
Develop and implement data quality checks and validation processes to ensure data accuracy, timeliness, and consistency.
Utilize ETL/ELT tools and techniques to transform and load data into target systems. - Implement and enforce data security best practices including access control, encryption, data masking to identify and safeguard sensitive data in accordance with respective regulatory requirements and internal policies.
- Maintain data catalogue for all data assets, and metadata lineage.
- Employ exceptional problem-solving skills, with the ability to see and solve issues before they snowball into problems.
- Learn and share knowledge and experience in a multi-disciplinary team.
Job Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
- At least 10 years of experience in data modelling, engineering, machine learning engineering, or a related role in a production environment.
- Highly proficient in Python and SQL. Experience with other programming languages (e.g., Java, Scala, C++) is a plus.
- Experience in best practices such as DevOps, DataOps and MLOps
- Experience with big data technologies and cloud platforms such as BigQuery, Kafka, GCP, AWS and their data engineering and machine learning products and services.
- Strong understanding of software development best practices, including version control (Git), testing, and CI/CD.
- Excellent communication and organizational skills, and the ability to stay focused on completing tasks and meeting goals within a busy workspace.
- Skilled at working in tandem with a team of engineers, or alone as required.
- Strong troubleshooting and analytical skills.
- Cloud and data certifications are a plus.
Version Control
Scalability
Scala
Big Data
Pipelines
Data Quality
Reliability
Access Control
Data Engineering
Snowball
GCP
Data Science
Metadata
Regulatory Requirements
Encryption
Data Warehousing
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