Full Stack Cloud Engineer

Job Title: Full Stack Cloud Engineer Primary Location: Madison, Wisconsin- Hybrid Position Type: Direct Hire

a Full Stack Cloud Engineer with our premier client.

This is a direct hire role that is hybrid in Madison, WI. Location Flexibility: Madison, WI area (within 50-mile radius): 3 days in-office per week

Chicago, IL area: Remote work with 1 day per week in Madison office Relocation assistance available for qualified candidates willing to move to the Madison, Wisconsin area.

This exciting opportunity to join our client's Data Services & Engineering Team in their efforts of supporting, implementing & developing industry-leading systems, and platforms to support a diverse and complex set of investment portfolios and strategies. The team strives to be a trusted advisor and partner to the business that is valued as a critical contributor to the organization's continued growth and success. This role will aid in the effort of effectively leveraging technology to derive the maximum value from it and achieve business goals. As well as keeping technology aligned with the organization's future direction and operating technology according to industry standards.

Bachelor's degree or advanced degree in finance, business, engineering, computer science, computational economics, math, data science or a related program.

3+ years of experience with data science, data analytics, investment analysis, or similar.

5-7+ years as a Full Stack Engineer with cloud experience-

must have experience and knowledge of Azure and AWS.

Minimum of 3 years of Investment Management industry experience.

Proactively drives data-driven decision-making through innovative analytical solutions and models. Exceptional verbal and written communication skills, adept at conveying complex data concepts to technical and non-technical stakeholders.

Proficient in programming languages such as Python, SQL, or R for data manipulation, analysis, and model development. Experience implementing data quality frameworks and conducting data validation ensuring accuracy of analysis.

Skilled in developing and deploying machine learning models, utilizing techniques such as regression, classification, and clustering. Knowledge of cloud platforms (e.g., Azure, AWS) for data storage and processing, with experience in deploying data solutions in cloud environments.

Experience with data warehousing technologies and platforms (e.g., Snowflake) to support analytics initiatives.

Experience deploying reports utilizing automated processes Continuous Integration and Continuous Deployment techniques (CICD).

Experience implementing testing tools and data quality metrics/processes to ensure overall data quality of reports that are supported and developed. Superb work ethic, attention to detail, team orientation, collaborative disposition, and commitment to excellence.

Interest or experience in investment management, quantitative finance, and technology. Progress toward or completion of the CFA designation is preferred.

Ability to follow consistency in creating/updating documentation, maintain process (i.e., JIRA tickets) and following technology and business best practices.

What You'll Do. (Skills Used in this Position)

Enable data-driven decision-making through innovative analytical solutions and models.

Convey complex data concepts to technical and non-technical stakeholders.

Utilize programming languages such as Python and SQL for data manipulation, analysis, and model development.

Act as a liaison between investment personnel and the supporting infrastructure regarding business process change management (IT, Operations, Legal, HR, Strategic Planning, etc.) Implements data quality frameworks and conducting data validation ensuring accuracy of analysis. Develop and deploy models, utilizing techniques such as regression, classification, and clustering. Create interactive visualizations (e.g., Power BI, Streamlit) to effectively communicate data findings. Deploy data solutions in cloud environments (e.g., Azure, AWS). Utilize data warehousing technologies and platforms (e.g., Snowflake) to support analytics initiatives.