Highlights
Build cutting-edge data solutions, partner with leading quant researchers, enjoy a results-oriented environment.
Description
Job Summary
pTower Research Capital, a leading quantitative trading firm, seeks a Business Support Engineer to build and own the end-to-end data layer for systematic equities trading. This role involves developing scalable pipelines, ensuring high-quality datasets, and partnering with quant researchers to enable alpha development.
Responsibilities
- Building and owning the end-to-end data layer from raw vendor data to research-ready datasets
- Developing scalable pipelines for ingestion, cleaning, normalization, and point-in-time accurate historical data
- Handling data nuances including revisions, biases, and inconsistencies to ensure high-quality inputs
- Partnering closely with quant researchers to enable alpha development through reliable datasets
- Establishing data validation, monitoring, and documentation standards
Required Skills
- Data Engineering
- Python Programming
- SQL
- Data Cleaning Techniques
- Hadoop/Spark Experience
Required Skills Explained
- Strong programming skills, proficiency in Python and SQL is essential
- Experience with data processing and manipulation tools such as Apache Spark or Dask
- Familiarity with financial market data and its nuances
- Knowledge of database management systems (DBMS) and their application in trading environments
- Absolutely proficient in version control systems like Git
- Understanding of low-latency programming principles is advantageous
Who is this for
pThis role is ideal for individuals with a strong background in data engineering and a passion for developing scalable systems. A collaborative mindset, coupled with excellent problem-solving skills, is essential.
Why This Job is a Good Opportunity
ulliOpportunity to work with cutting-edge technology and contribute to the development of proprietary trading algorithmsliPotential for growth in a company that values innovation and performanceliAccess to diverse datasets and research opportunitiesliCollaborative environment fostering teamwork and mutual support among colleaguesliHybrid work arrangements offering flexibility while maintaining a vibrant corporate cultureliGlobal presence with numerous offices providing varied career experiences
Interview Preparation Tips
- Prepare examples of how you have handled complex data challenges in past roles
- Showcase your ability to work independently and within a team
- Be ready to discuss your knowledge of financial markets, especially equities trading
- Highlight any experience with low-latency programming or FPGA technology if applicable
- Demonstrate your understanding of data validation techniques and standards
- Bring examples of open-source contributions or personal projects related to data processing and analysis
Career Growth in This Role
pCareer growth at Tower Research Capital is driven by individual performance, innovation, and continuous learning. Success in this role can lead to advancements into more specialized technical roles or even managerial positions overseeing larger teams of data engineers. The company’s emphasis on fostering a culture that values both performance and enjoyment ensures there are numerous opportunities for professional development and skill enhancement.pAdditionally, Tower offers workshops and continuous learning opportunities which can help you stay updated with the latest technologies in data engineering and quantitative trading.
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Skills
Frequently Asked Questions
What kind of data pipelines will I be developing?You will develop scalable pipelines for ingestion, cleaning, normalization, and point-in-time accurate historical data.
How does Tower support its employees in terms of career growth?Tower fosters a culture of continuous learning with workshops and training opportunities to help you grow professionally.
What benefits are available for employees at Tower Research Capital?Employees enjoy generous paid time off, hybrid working options, free daily meals, wellness programs, and volunteer opportunities.
