Product Manager

Sapient Corporation; Chicago (IL); Hybrid; USD 122,000 - 128,000; Full time

Job summary

Sapient Corporation is seeking a Product Manager in Chicago to design and maintain analytics solutions for investment portfolios. You will work cross-functionally to ensure data integrity and accurate performance reporting across platforms.

You will lead API integrations and dashboard development, translating financial concepts into actionable product requirements for engineering teams. Hybrid work options are available.

Qualifications

• Experience applying investment analytics concepts including portfolio performance measurement and risk exposure metrics.
• Experience working with enterprise investment platforms like Aladdin/Charles River/Bloomberg.
• Ability to gather and document functional and technical requirements for complex analytics solutions.
• Design, test, and document REST APIs for data integration.

Responsibilities

• Design and maintain enterprise analytics solutions for portfolio performance and risk tracking.
• Integrate platforms to ensure trade records, pricing data, benchmarks, and holdings flow across systems.
• Translate complex financial concepts into product requirements for engineering teams.
• Develop data pipelines moving trade and market data via API-based integrations.
• Create dashboards to verify valuations and risk figures before reporting to fund managers.

Skills

Portfolio analytics

Risk measurement

Financial data reconciliation

SQL

Dashboard design

Education

Bachelor’s degree in finance/systems engineering/economics/data analytics

Tools

Tableau

Power BI

Postman

Swagger

GitHub/Bitbucket

Job description

Employer

Sapient Corporation

Job Title

Product Manager

Job Requisition

6630.7427.16

Job Location

Will work from Chicago, IL and various unanticipated client sites and Sapient offices nationally, Telecommuting available on a hybrid basis at company discretion

Job Type

Full Time

Rate of Pay

$121,957.50 per Year to $128,055.38 per Year

Duties

Design and maintain enterprise analytics solutions that track investment portfolio performance, risk exposure, and returns for asset management clients, ensuring the underlying data is validated and reconciled across sources; Integrate enterprise investment management platforms such as Aladdin, Charles River, State Street Alpha, and Bloomberg to ensure that trade records, pricing data, benchmarks, and portfolio holdings flow accurately across systems; Collaborate with investment and risk teams to translate complex financial concepts - such as measuring portfolio risk, valuing fixed income instruments, or modeling stress scenarios - into detailed product and system requirements for engineering teams; Design data pipeline systems that move trade and market data across investment platforms using API-based integrations via tools such as Postman, and cloud infrastructure; Query investment databases using SQL and develop dashboards in tools such as Tableau or Power BI to verify that portfolio valuations, risk figures, and performance results are accurate before they reach fund managers or compliance teams; Use project tracking tools such as JIRA to manage the product backlog, plan delivery sprints, and monitor engineering progress; document platform requirements and technical decisions in Confluence or similar tools for team reference; Investigate and resolve issues in live production systems where financial data is incorrect, missing, or inconsistent across sources, restoring data integrity for investment and operations teams that depend on the platform; Maintain and version-control product configuration files, technical specifications, and pipeline code using Git-based systems such as GitHub or Bitbucket in collaboration with engineering teams; Apply financial analytics methodologies including portfolio risk analysis, performance attribution, fixed income analytics, and valuation techniques such as VaR and stress testing to define and validate investment platform requirements and outputs; and Evaluate emerging technologies in cloud computing, data engineering, and machine learning, and recommend solutions that can automate financial reporting workflows or improve the speed and accuracy of analytics delivery.

Employer Qualification

Employer will accept a bachelor s degree in finance, systems engineering, data analytics, economics or related followed by three years of progressive, post-baccalaureate experience in the job offered or three years of progressive, post-baccalaureate experience in any occupation in which the required experience was gained

#LI-DNI

#LI-DNI

Qualifications

1. Experience applying investment analytics concepts including portfolio performance measurement, risk exposure metrics, valuation analytics, financial data reconciliation, and analysis of multi-asset portfolio datasets.
2. Experience working with financial datasets and platforms including Aladdin, Charles River, State Street Alpha, Bloomberg, or similar enterprise investment systems, and trade, pricing, benchmark, and portfolio position data.
3. Gathering and documenting functional and technical requirements for complex portfolio risk measurement, enterprise financial analytics or investment management software solutions.
4. Designing, integrating, and testing REST APIs using tools such as Postman or Swagger to support API-driven data integration across financial platforms.
5. Analyzing datasets using SQL and developing dashboards with Tableau, Power BI, or similar visualization tools.
6. Using JIRA or similar Agile tools for backlog management, sprint planning, and project tracking. Using Confluence or similar platforms to develop product documentation and technical specifications.
7. Investigating and resolving production issues including data inconsistencies, market data discrepancies, and analytics calculation errors in financial analytics platforms.
8. Using Git-based version control systems such as GitHub, Bitbucket, or similar tools for collaborative development.
9. Experience applying financial analytics methodologies including portfolio risk analysis, performance attribution, fixed income analytics, derivatives valuation, Value-at-Risk (VaR), stress testing, and valuation analytics.
10. Evaluating emerging technologies including cloud computing, data engineering frameworks, and machine learning tools to improve analytics automation and reporting workflows.

Additional Information

Will work from Chicago, IL and various unanticipated client sites and Sapient offices nationally, Telecommuting available on a hybrid basis at company discretion 6630.7427.16