Data Analyst, Investment Professional

Nationwide•Columbus, OH

1d•$127,000 - $213,000•Hybrid

About The Position

As a team member in Finance at Nationwide, a Fortune 100 company with nearly $70 billion in annual sales, the opportunities are endless! Let Nationwide help create your career journey! At Nationwide®, “on your side” goes beyond just words. Our customers are at the center of everything we do and we’re looking for associates who are passionate about delivering extraordinary care. Do you thrive on making sense of complex data and transforming it into insight that drives business impact? Are you energized by working at the crossroads of analytics, technology, and investment strategy? If solving challenging data problems in a highly collaborative, mission-driven environment excites you, this is the job for you. As a Consultant, Investment Professional Data Analyst, you will play a critical role in shaping how Nationwide Investments leverages data to deliver smarter, faster, and more consistent decisions. You’ll build the analytic structures and reusable data products that power front-office dashboards, AI-driven workflows, and quantitative tools. You’ll apply your technical expertise and collaborate with your team to design high-impact solutions that matter in a multi-billion-dollar investment portfolio environment.

Requirements

Bachelor’s degree in computer science, information systems, data analytics, mathematics, engineering, or a related field preferred.
Advanced degree or relevant certifications preferred.
Typically, eight or more years’ experience building and supporting analytical data, business intelligence, or investment data environments.
Experience working with enterprise data warehouses, analytical reporting, and reusable data models in a complex business setting is preferred.
Experience in financial services, asset management, insurance, or capital markets is desirable.
Experience supporting AI-enabled analytics, analytics engineering workflows, or governed automation in an enterprise environment is preferred.
Proven advanced SQL and data-modeling capability with experience building complex queries and robust analytical data models.
Hands-on experience with Snowflake and comfort working in modern analytics environments such as Databricks.
Practical Python skills for data engineering and analytics (pandas, PySpark, or similar).
Demonstrated experience designing and maintaining analytics or semantic layers for BI and quantitative use cases.
Experience publishing reusable datasets to BI tools such as Power BI/Tableau, including dataset design and performance tuning.
Working familiarity with investment data structures such as positions, transactions, benchmarks, and reference data.
Understanding of data governance, lineage, quality monitoring, and version control practices.
Ability to work cross-functionally and communicate technical concepts clearly.
Familiarity with automation or AI-enabled data workflow techniques with human oversight.
Knowledge of software engineering practices relevant to analytics environments, including Git, pull requests, and documentation.
Ability to balance speed, control, transparency, and maintainability.
Curiosity and comfort in tech-evolving team environments.

Responsibilities

Design curated analytic data structures and shared business logic, including sectors, ratings, strategy tags, benchmarks, and other core dimensions, in partnership with Data Governance and Data Management.
Provide controlled, reusable data access through SQL, APIs, and related interfaces for quantitative analysts, portfolio manager, enterprise portfolio managers, reporting teams, enabling a consistent source of truth for investment analytics.
Build and maintain the analytics and semantic layer that supports front-office dashboards, quantitative tools, and research workflows.
Monitor and improve the quality, consistency, and documentation of analytical data and reports that sit on top of enterprise golden sources.
Support data needs for AI and agentic workflows in a governed, auditable way, including input/output structuring and data persistence.
Collaborate with Technology and platform teams to align the analytics layer with enterprise architecture, including Snowflake, Databricks, and Aladdin.
Support continuous improvement of data, tooling, and reporting processes across the investment analytics function.
Build analytics-ready datasets and reusable data products for reporting, research, quantitative models, and evolving AI-enabled workflows, rather than one-off extracts or bespoke data pulls.
Apply automation and AI-enabled techniques where appropriate to improve data ingestion, tagging, reconciliation, anomaly detection, metadata capture, and documentation, with human review and appropriate controls.
Help improve coding, testing, version-control, and documentation practices for the production of data pipelines, analytical datasets, and related workflow tools.
Design data assets and interfaces that are explainable, documented, and reusable across front office teams, while reducing manual spreadsheet-based processing and redundant logic.
Support the evolution of the investment analytics architecture by ensuring data structures, mappings, and lineage are compatible with changing enterprise platforms, workflows, and future-state needs.
Contribute to a collaborative team environment that emphasizes experimentation, shared tooling, documentation, and continuous improvement across data and analytics processes.
May perform other responsibilities as assigned.