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Well-known asset manager in a metro city but senior, specialized BI role reduces general applicant density.
Requires deep alternative-investment reporting expertise, limiting cross-industry transferability.
Explicit 10+ years, required alternative-investment domain experience and specific BI/data engineering tech stack.
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Lead design and maintenance of cloud-ready data pipelines and dimensional models using SQL, Python, Power BI, and cloud services (Microsoft Fabric/Azure/AWS) for alternative investment strategies.
Develop and operationalize scalable ETL/ELT workflows and BI dashboards to improve data quality, reporting speed, and production controls across private markets and enterprise reporting.
Partner with cross-functional teams to translate business requirements into technical solutions, oversee junior team members, and ensure auditable, repeatable reporting outputs.
10+ years of experience in data engineering, BI development, reporting automation, or analytics engineering, preferably within asset management, financial services, or comparable domains.
Mandatory domain experience in alternative investments; preferred exposure includes asset-based finance, structured credit, private credit, opportunistic credit, emerging markets debt.
Proficiency with SQL, Python, Power BI (including DAX, Power Query/M, paginated reports), and cloud data platforms (Microsoft Fabric/Azure and/or AWS services).
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Finance, or a related analytical field, or equivalent work experience.
Experienced in scalable data platform architecture and enterprise BI solutions within alternative investment or asset management environments, demonstrating strong domain expertise in credit and structured finance.
Capable of driving technical delivery and strategy, mentoring junior staff, and collaborating effectively with investment and technology stakeholders across geographies.
Detail-oriented with strong data governance knowledge and a builder’s mindset focused on replacing manual processes with automated, controlled data products.