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Senior role but popular data-engineer title, metro location and broad skillset create medium competition.
Strong asset-management data experience required, making industry-specific skills highly important.
Mandated 12+ years, Azure/DBT/Python expertise and finance domain experience creates high shortlisting strictness.
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Integrate and operationalize data engineering frameworks to ensure scalable, sustainable data operations supporting Oaktree's credit business.
Build and maintain production-grade ETL/ELT data pipelines, data quality rules, and monitoring dashboards with high ownership and autonomy, collaborating with global stakeholders.
Drive continuous improvement using modern engineering practices (CI/CD, testing, observability) and evolving Azure cloud platform, including leveraging generative AI for efficiency and product enhancement.
12+ years experience in data operations, data quality, and system/job performance monitoring within fast-paced environments.
Proficient in SQL, Python, .NET; working knowledge of Azure cloud services (Azure DWH, ADLS, Azure DevOps, Azure Monitor, Azure Purview).
Bachelor's degree in business, engineering, computer science, or related field.
Work Experience Required: 12+ years relevant experience. Notice period: Not explicitly mentioned in the JD.
Experienced hands-on senior data engineer with ability to independently take ownership from problem identification to solution delivery in a data-intensive environment.
Comfortable working across technical and business stakeholders, including global teams primarily based in Los Angeles, managing end-to-end data product delivery.
Skilled in designing scalable, reliable data platforms and operational processes in cloud (Azure) environments, with practical experience in data governance and data operations automation.