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.