Phaxis
Senior Data Engineer
Phaxis, New York, New York, us, 10261
SENIOR DATA ENGINEER AT BOUTIQUE ANALYTICS FOCUSED HEDGE FUNDBase Salary is $175K to $200K (based upon candidate and experience); Plus Bonus; and Excellent BenefitsIncredible Organization with Equally Incredible People | New York (SoHo), NY | Onsite Four (4) Days per Week
Multi-office, solidly established, international, boutique analytics-focused hedge fund is looking for an
experienced data engineer
to join its growing team in their New York (SoHo), NY Office. In this role, you will help enhance the capabilities of the firm's Data Platform. The Data Engineering Team is responsible for building and maintaining our storage infrastructure and Extract/Transform/Load (ETL) processes. This team also serves as the owner for the data collected by all lines of business and supports the interface layer that delivers data for users and applications across the front, middle, and back office.
REQUIREMENTS AND SKILLS
Five (5) years of Experience in a Data Engineering Role
required
.Strong Data Engineering Background - ETL, Cloud Database Tech (AWS, Redshift, etc.), Developing Data pipelines.Strong Python with experience using packages (Client/Data science packages not needed).Exposure to object-oriented programming (OOP).Strong SQL skills.Some exposure to advanced relational databases concepts (i.e. stored procedures, indexing, partitioning, table design, query optimization).Commodities Fundamental Data (Gas, Power, FTR) Domain experience
huge plus
.Comfortable working with a team and individually.Experience working with large datasets.Ability to execute independently, on a deadline, and under pressure.Excellent troubleshooting and analytical skills.Some knowledge of cloud environments.Experience with automation/orchestration platforms (Airflow, CRON, etc.).Experience with version/source control systems (Git).
RESPONSIBILITIES AND DUTIES
Build, maintain and enhance platform components.Build infrastructure to make data sets accessible and useful.Develop tools and processes for automated acquisition, validation, and organization of large volumes of structured and unstructured data from many sources.Develop systems to improve and test data quality.Maintain a strong engineering culture and high code quality standards.Produce documentation for technical and non-technical audiences.Architect, develop and maintain solutions for business teams and help them do the same.
Multi-office, solidly established, international, boutique analytics-focused hedge fund is looking for an
experienced data engineer
to join its growing team in their New York (SoHo), NY Office. In this role, you will help enhance the capabilities of the firm's Data Platform. The Data Engineering Team is responsible for building and maintaining our storage infrastructure and Extract/Transform/Load (ETL) processes. This team also serves as the owner for the data collected by all lines of business and supports the interface layer that delivers data for users and applications across the front, middle, and back office.
REQUIREMENTS AND SKILLS
Five (5) years of Experience in a Data Engineering Role
required
.Strong Data Engineering Background - ETL, Cloud Database Tech (AWS, Redshift, etc.), Developing Data pipelines.Strong Python with experience using packages (Client/Data science packages not needed).Exposure to object-oriented programming (OOP).Strong SQL skills.Some exposure to advanced relational databases concepts (i.e. stored procedures, indexing, partitioning, table design, query optimization).Commodities Fundamental Data (Gas, Power, FTR) Domain experience
huge plus
.Comfortable working with a team and individually.Experience working with large datasets.Ability to execute independently, on a deadline, and under pressure.Excellent troubleshooting and analytical skills.Some knowledge of cloud environments.Experience with automation/orchestration platforms (Airflow, CRON, etc.).Experience with version/source control systems (Git).
RESPONSIBILITIES AND DUTIES
Build, maintain and enhance platform components.Build infrastructure to make data sets accessible and useful.Develop tools and processes for automated acquisition, validation, and organization of large volumes of structured and unstructured data from many sources.Develop systems to improve and test data quality.Maintain a strong engineering culture and high code quality standards.Produce documentation for technical and non-technical audiences.Architect, develop and maintain solutions for business teams and help them do the same.