Skills & Experience:
At least 5+ years of experience as a Data Engineer
Hands-on and in-depth experience with Star / Snowflake schema design, data modeling,
data pipelining and MLOps.
Experience in Data Warehouse technologies (e.g. Snowflake, AWS Redshift, etc)
Experience in AWS data pipelines (Lambda, AWS glue, Step functions, etc)
Proficient in SQL
At least one major programming language (Python / Java)
Experience with Data Analysis Tools such as Looker or Tableau
Experience with Pandas, Numpy, Scikit-learn, and Jupyter notebooks preferred
Familiarity with Git, GitHub, and JIRA.
Ability to locate & resolve data quality issues
Ability to demonstrate end to ed data platform support experience
Other Skills:
Individual contributor
Hands-on with the programming
Strong analytical and problem solving skills with meticulous attention to detail
A positive mindset and can-do attitude
To be a great team player
To have an eye for detail
Looking for opportunities to simplify, automate tasks, and build reusable components.
Ability to judge suitability of new technologies for solving business problems
Build strong relationships with analysts, business, and engineering stakeholders
Task Prioritization
Familiar with agile methodologies.
Fintech or Financial services industry experience
Eagerness to learn, about the Private Equity/Venture Capital ecosystem and associated
secondary market
Responsibilities:
o Design, develop and maintain a data platform that is accurate, secure, available, and fast.
o Engineer efficient, adaptable, and scalable data pipelines to process data.
o Integrate and maintain a variety of data sources: different databases, APIs, SAASs, files, logs,
events, etc.
o Create standardized datasets to service a wide variety of use cases.
o Develop subject-matter expertise in tables, systems, and processes.
o Partner with product and engineering to ensure product changes integrate well with the
data platform.
o Partner with diverse stakeholder teams, understand their challenges and empower them
with data solutions to meet their goals.
o Perform data quality on data sources and automate and maintain a quality control
capability.
