Software Engineer – Data
Position Details
Our client is an active investment management firm focused on delivering attractive performance and client portfolio solutions. They believe that technology will play a key role in the future of finance, and have built a robust trading platform to handle scale, complexity and customisation.
Their systems are almost all running on Linux and most of the code is in Python, with the full scientific stack – numpy, scipy, and pandas being some of the libraries they use extensively. The systems that require the highest data throughput are implemented in Java. Within Data Engineering they use Dataiku, Snowflake, Prometheus, and ArcticDB heavily. They use Kafka for data pipelines, Apache Beam for ETL, Bitbucket for source control, Jenkins for continuous integration, Grafana + Prometheus for metrics collection, ELK for log shipping and monitoring, Docker for containerisation, Kubernetes for container orchestration, OpenStack for our private cloud, and Ansible for architecture automation.
The Role
This role sits within the Data Engineering team which develops and maintains tools for a range of data-related activities including onboarding, analysis, sourcing, quality checking, and lifecycle management. Your challenges will be varied, and might involve:
- Developing and maintaining core tools for analysts, quants, and engineers to on-board and
analyse datasets at multi-terabyte-scale. - Collaborating with the Data Science team as they design and develop unique, bespoke
solutions to solve big data challenges. - Designing and implementing strategies and tools to monitor and validate the data quality
for thousands of datasets in use. - Discovering and leveraging best-in-market 3rd party tools and cloud technologies that can
help to optimise the full data pipeline from scouting to trading.
Job Requirements
Essential
- Strong academic record and a degree with high mathematical and computing content e.g.
Computer Science, Mathematics, Engineering or Physics - Extensive programming experience, ideally in Python
- Knowledge of the challenges of dealing with large data sets, both structured and unstructured
- Knowledge of modern practices for ETL, data engineering and stream processing
- Proficient on Linux platforms with knowledge of various scripting languages
- Working knowledge of one or more relevant database technologies e.g. MongoDB, PostgreSQL, Snowflake, Oracle
- Proficient with a range of open source frameworks and development tools e.g. NumPy, SciPy, Pandas, Spark, Jupyter
Advantageous
- Prior experience of working with financial market data or alternative data
- Relevant mathematical knowledge e.g. statistics, time-series analysis
- Experience in data visualisation and building web apps in modern frameworks e.g. React
- Experience with git
- Prior experience with AWS
