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Lead Data Engineer

2 weeks ago

Singapore Hermeneutic Investments Full time

**About Us / Why Join?**

We are a rapidly growing crypto hedge fund, 2 years old, managing a 9-figure AUM, generating 200%+ annualized returns with a 4 Sharpe.

We've grown to a team of approximately 40 professionals across Trading & Research, Technology, and Operations.

**About the Role**

**Responsibilities**
- **Data Architecture and Infrastructure Design**: Lead the design, architecture, and implementation of data pipelines capable of handling large volumes of market data and real-time news feeds. Ensure the infrastructure supports trading and research needs while maintaining data integrity, security, and performance at scale.
- **Data Integration and Management**: Architect solutions for ingesting, storing, and integrating structured and unstructured data from multiple sources including market feeds, historical data, and external news feeds. Develop processes to normalize and organize this data for use across different departments.
- **Support Research and Analytics Teams**: Collaborate with research and analytics teams to understand their data needs and build frameworks that empower data exploration, analysis, and model development. Create tools for overlaying data from multiple sources and identifying relationships to inform trade ideation and execution.
- **Real-Time Data Processing**: Design and implement systems capable of handling high-frequency data streams, including real-time market data and news, ensuring that the trading and research teams can act on insights as they emerge.
- **Cost Efficiency**: Ensure that data storage, processing, and management are done in a cost-effective manner, optimizing both hardware and software resources. Implement solutions that balance high performance with cost control.
- **Leadership and Team Management**: Lead and grow the data engineering team, providing mentorship, guidance, and support to foster a collaborative and innovative work environment. Drive strategic decision-making and set clear objectives for the team’s performance.
- **Cross-Department Collaboration**: Work closely with various teams, including trading, research, and IT, to ensure seamless integration of data infrastructure and alignment with business goals.
- **Technology Evaluation and Selection**: Stay ahead of the curve by continuously evaluating and adopting the most suitable technologies for the organization’s data engineering needs. Ensure that the systems align with the latest best practices in data management.

**Requirements**:
**Must Haves**
- **Technical Leadership**: Proven experience leading and managing data engineering teams with a focus on developing and scaling data pipelines for high-frequency or time-sensitive data (e.g., market data).
- **Data Architecture Expertise**: Strong background in designing data architectures capable of handling large-scale data, including structured, semi-structured, and unstructured data sources.
- **Real-Time Data Processing**: Hands-on experience with real-time data streaming, data ingestion, and processing frameworks.
- **Data Integration**: Expertise in integrating multiple data sources and platforms, ensuring smooth interoperability between different systems and data formats.
- **Cost Optimization**: Ability to balance performance and cost efficiency when designing data infrastructures, including storage, compute, and network costs.
- **Analytical Thinking**: Strong problem-solving skills and ability to analyze complex datasets to identify meaningful relationships, trends, and insights.
- **Programming Languages**: Expertise in programming languages commonly used in data engineering, such as Python, Java, or Scala.
- **Database Management**: Strong experience with data storage solutions (e.g., relational databases, NoSQL databases, and distributed file systems like Hadoop or Apache HDFS).
- **Data Storage & Management Techniques**:

- **Sharding** for distributed database management
- **Replication** to ensure high availability
- **Partitioning** for performance optimization
- **Indexing** for faster query performance
- **Data Compression** to save storage space and improve I/O efficiency
- **Scalability & High Availability**: Proven expertise in designing and maintaining scalable and highly available data infrastructures, ensuring systems can grow with data needs and remain operational even under heavy load or during failures.
- **Security and Compliance Expertise**: Knowledge of security protocols and standards, including encryption, role-based access controls (RBAC), and secure data management. Familiarity with regulatory requirements such as GDPR, CCPA, or industry-specific regulations is a plus.
- **Data Governance & Quality Control**: Strong experience in implementing data governance best practices, including managing data lineage, ensuring high data quality, and establishing clear data management processes.
- **Data Modeling & Metadata Management**: Experience designing efficient and scalable data models and managing metadata to en