Principal Data Engineer (AWS, Python, Spark) | Elite Quant Investment Firm | Boston
One of Boston's top Quantitative Investment Firms has poured millions of dollars into transforming the technology that powers their quantitative trading systems; these transformations have fueled industry leading success over the past 5 years, and their assets under management have increased by more than $100+ billion in that time period. They are rapidly migrating to AWS and building next generation data systems that are highly automated, scalable, and secure. These systems will handle petabytes of complex market data, supporting multiple types of equities and securities.
This role will design highly performant solutions that model data for this firm's mission critical trading systems. You will work closely with a small team of senior engineers and collaborate with the business to ensure your solutions are scalable enough to react to changes and opportunities in the market in real time. You will be working in a greenfield environment to build systems and models from scratch, and will be responsible for design, development, and delivery.
Key Responsibilities:
- Design and build next generation cloud native data systems in a greenfield AWS environment
- Enhance existing processes to ensure performance, availability, and data quality are optimized
- Define best practice and act as a thought-leader throughout the firm on AWS adoptions
Required Qualifications:
- BS or equivalent degree
- 6+ years of hands-on experience building data ingestion systems, pipelines, and/or models
- 3+ years of experience with Spark and Python
- Expertise with SQL and Pandas/Numpy
- Experience designing solutions with a range of AWS Services
- Some experience with Kubernetes, C#, and Pytest is preferred but not required
Capitalize on:
- Extremely competitive employee benefits program, 401K, and compensation package
- One of the best-rated work cultures and work/life balances in the business
- Casual dress, collaborative teams, and work from home flexibility
