Overseeing the full life-cycle and back-end development of the brokerage business’ data warehouse. This role will be responsible for developing ETL processes and pipelines, on-boarding new system feeds, administering our KDB time-series database, managing downstream data feeds and ensuring a fault tolerant data architecture.
The data engineering teams will work closely with data analysts, the electronic trading technology team, and product management in order to ensure data is available so teams across the organization are able to generate analytics and insights to make informed management decisions.
- Develops and maintains scalable data pipelines to support continuing increases in data volume and complexity.
- Collaborates with analytics and business teams to improve data models that feed business intelligence tools, increasing data accessibility and fostering data-driven decision making across the organization.
- Implements processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it.
- Performs data analysis required to troubleshoot data related issues and assist in the resolution of data issues.
- Works closely with all business units and engineering teams to develop strategy for long term data platform architecture.
- Degree holder of a Computer Science, Statistics, Informatics, Information Systems or another quantitative discipline.
- Familiar with OOP programming languages such as Java or Python.
- 2+ years experience managing KDB systems.
- Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
- Following TDD/BDD methodologies and clean coding practices.
- Experience working in a Linux environment.
- Comfortable working with developer tools such as GIT, Jira and Confluence.
- Familiar with automated build, test and deployment pipelines (e.g. GitLab or Jenkins).
- Strong problem-solving skills and a confident communicator.
- Ability to work independently and comfortably to tight schedules.