Data Analyst, Data Ops

Harris Associates•Chicago, IL

13h•$95,000 - $110,000

About The Position

At Harris, the true value of what makes us successful is found in our people. It is our unique mix of cultures, experiences, beliefs and backgrounds that sets Harris apart from the rest. We constantly strive to cultivate, nurture and amplify an unparalleled environment, where we value intellectual curiosity and uniqueness of thought. Inclusion is embedded in the very fabric of our culture of collaboration and openness. We understand that a job description only tells one part of a broader story, and Harris is seeking dynamic candidates who can add to our best-in-class environment. We recognize that qualifications can be gained through both traditional and non-traditional paths, and we are committed to considering candidates who possess the potential to be excellent in this role regardless of prior experiences. Therefore we encourage ALL interested individuals to submit their applications, even if they do not meet every requirement outlined in the job description. Position Summary: The Data Ops team is responsible for delivering scalable data, reporting and operational analytics solutions that support decision-making across the firm. The team partners closely with stakeholders to enable trusted, well governed data that improves transparency, efficiency, and outcomes. The Data Analyst, Data Ops is responsible for ensuring the accurate, timely and reliable delivery of data and analytics solutions to support process efficiency and data-centric decision making . This role partners cross functionally to design, build and enhance data, reporting and workflow automation solutions. The Data Analyst supports enterprise analytics, BI, and self-service initiatives by ensuring data is consistently modeled, documented, governed and aligned with operational risk, audit, and regulatory expectations. Reporting to the Senior Director of Business Process and Data Ops, the ideal candidate combines strong analytical and problem-solving skills with an operational mindset, brings a solid understanding of investment management data domains and thrives in a collaborative, agile environment.

Requirements

Bachelor’s degree in Data Analytics, Engineering, Computer Science, Business, or a related field.
4-6 years of experience in investment management, financial services, or a data intensive operations environment.
Experience with cloud platforms (Snowflake, Azure), data quality tools, data catalogs, and BI platforms (PowerBI, Tableau).
Advanced proficiency with SQL and Python.
Experience working with large datasets and using analytical tools to summarize, interpret, and deliver business value.
Demonstrated ability to analyze data issues, identify root causes, and drive cross-functional resolution.
Exceptional attention to detail, initiative, and adaptability, with a proactive approach to identifying data anomalies, process gaps, and improvement opportunities.
Strong communication skills with the ability to explain complex data concepts to nontechnical stakeholders.

Nice To Haves

Familiarity with Agile methodology and project management practices is a benefit.
Experience with data engineering, pipeline development, JIRA, SharePoint, or workflow automation tools is a plus.

Responsibilities

Partner with data consumers to understand data needs and ensure accuracy, reliability, and scalability of data and reporting outputs.
Design, develop, and maintain sophisticated data, reporting, and workflow automation solutions.
Execute high-impact, cross-functional data initiatives from requirements through delivery.
Gain deep expertise in firm-wide business processes and translate operational needs into scalable solutions.
Support BI, reporting, and self-service analytics initiatives by ensuring consistent, well-modeled, and documented data.
Support enterprise data governance by adhering to data standards, definitions, and stewardship practices.
Contribute to the design and documentation of standard operating procedures, data controls, and issue management processes.
Ensure data processes align with operational risk, audit, and regulatory expectations.
Participate in firm-wide initiatives such as system implementations and data integrations.
Participate in agile planning, backlog refinement, and prioritization of DataOps initiatives.
Foster strong cross-functional relationships and promote a culture of collaboration, accountability and continuous improvement.