Blue Owl (NYSE: OWL) is a leading asset manager that is redefining alternatives®.

With $273 billion in assets under management as of March 31, 2025, we invest across three multi-strategy platforms: Credit, GP Strategic Capital, and Real Assets. Anchored by a strong permanent capital base, we provide businesses with private capital solutions to drive long-term growth and offer institutional investors, individual investors, and insurance companies differentiated alternative investment opportunities that aim to deliver strong performance, risk-adjusted returns, and capital preservation.

Together with over 1,200 experienced professionals globally, Blue Owl brings the vision and discipline to create the exceptional. To learn more, visit www.blueowl.com.

The Role:

The GP Stakes business takes minority ownership stakes in other institutional asset managers (referred to as “Partner Managers”). The existing portfolio consists of 60+ partnerships across a diversified set of GPs with strategies primarily including private equity, public and private credit, and real assets. Our differentiated approach features a robust value creation team called the Business Services Platform, comprised of 60+ professionals globally that provide strategic support to Partner Managers to help drive enterprise value. The Business Services Platform provides support across Client Development and Fundraising, Business Strategy and Growth, and Digital Transformation & Optimization.

In this Data / Analytics Engineer role within GPSC BSP Portfolio Intelligence team, you will own the end-to-end life cycle of BSP’s data assets—from intake and validation through modeling, storage, and distribution. Working side-by-side with our AI team, data scientists, and technology partners, you will ensure that high-quality, decision-ready data is always at our fingertips. This role will be a pivotal player in building the data foundation to power mission-critical insights and updates for the broader BSP team.

Responsibilities:

1. Data Collection, Aggregation & Pipeline Management

• Engage stakeholders to inventory required metrics, refresh cadences, and data quality thresholds.

• Map existing internal data to requirements; identify and onboard incremental sources from partners and vendors.

• Ingest structured and unstructured data from third-party systems (e.g. Salesforce, Qualtrics, etc.), partner data rooms, portfolio monitoring call notes, and other systems.

• Build and maintain ETL/ELT pipelines landing data from external systems in a data warehouse.

• Create and enforce data dictionaries, lineage documentation, and field-level ownership.

• Monitor pipeline health daily; troubleshoot failures, outliers, and schema changes in real time.

2. Data Governance & Quality

• Define validation rules, golden sources, and stewardship processes.

• Run checks to detect duplicates, missing values, and integrity exceptions.

• Publish regular data quality scorecards; drive remediation with content owners.

• Maintain appropriate role-based access controls.

3. Analysis, Visualization & Self-Service Enablement

• Partner with the Data Science & AI teams to model data for self-service use.

• Design dashboards, tear sheets, and benchmarking packs that highlight partner and portfolio performance.

• Prototype lightweight web or mobile UIs that surface critical insights on demand

• Leverage natural-language and generative-AI tools (e.g., LLMs, Azure OpenAI,

Amazon Bedrock) to summarize call notes, parse PDFs, & draft commentary.

Skillset:

1. Technical

• Advanced Python and SQL, experience working with cloud data warehouses (ex. Snowflake, Databricks).

• Familiarity with modern data stack (ex. dagster/Airflow, dbt/SQLmesh, spark, etc.)

• DevOps experience, familiarity building CI/CD pipelines (git, AWS, Docker)

• Experience with data governance best practices, including data lineage/documentation, access control, integrity testing

• Experience withBI/dashboard development (ex. Tableau/PowerBI, Apache

Superset); application development experience a plus (ex. Javascript frameworks, HTML/CSS)

• Familiarity with generative AI, LLM APIs, embeddings, vector databases, and prompt engineering basics.

2. Experience integrating SaaS business systems (Salesforce, Preqin, Qualtrics, etc.)

Business & Soft Skills:

• Ability to translate business questions into data requirements and vice versa. • Strong analytical mind-set with meticulous attention to detail.

• Excellent written and verbal communication; can create concise data briefs for senior (non-technical) leadership.

• Project-management discipline; comfortable juggling multiple deadlines.

• Collaborative spirit—you enjoy being the connective hub across teams.

Qualifications:

• Bachelor’s degree in Data Science, Computer Science, Information Systems, Engineering, Finance, or related discipline.

• 3+ years’ experience in data management, data engineering, business intelligence, or analytics (financial services or private-equity environment a plus).

• Demonstrated success building production-grade data pipelines and dashboards that influence business decisions.

• Exposure to investment, portfolio monitoring, or KPI benchmarking concepts preferred.

It is expected that the base annual salary for this New York City- based position will be $185,000. Actual salaries may vary based on factors, such as skill, experience, and qualification for the role. Employees may be eligible for a discretionary bonus, based on factors such as individual and team performance.