Data Engineer

Design, develop, and maintain scalable data pipelines and ETL workflows.
Build and optimize data architectures to support advanced analytics and real-time decision-making.
Integrate, clean, and organize large datasets from various structured and unstructured sources, including market, alternative, and proprietary data.
Collaborate with analysts, quants, and portfolio managers to ensure seamless data access and usability.
Monitor and troubleshoot data systems to ensure reliability and performance.
Implement best practices for data security, storage, and governance.
Explore and integrate new technologies and tools to enhance data processing and analytics.

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
Proven experience in data engineering, preferably in a financial or investment environment.
Expertise in Python, Java, or Scala for data processing.
Strong SQL skills and experience working with relational and non-relational databases (e.g., PostgreSQL, MongoDB).
Proficiency with big data tools and frameworks such as Spark, Hadoop, Kafka, or similar.
Experience with cloud platforms (e.g., AWS, Azure, GCP) and infrastructure-as-code tools (e.g., Terraform, CloudFormation).
Familiarity with data workflow tools like Apache Airflow or Luigi.
Strong understanding of data modeling, ETL design, and data pipeline automation.

Experience with financial market data and tools (e.g., Bloomberg, Reuters).
Knowledge of containerization and orchestration tools like Docker and Kubernetes.
Familiarity with real-time data processing and streaming analytics.
Exposure to alternative data sources and APIs.
Understanding of machine learning workflows and deployment pipelines.

Competitive salary and performance-based bonuses.
A fast-paced, intellectually stimulating environment.
Opportunities to work with cutting-edge technologies and solve challenging problems.
Professional growth and skill development opportunities.