Data platform transformation lead

Job Title: Data Platform Transformation Lead

Employment Type: Full‑time

Work Location: Hybrid in Denver, CO 80206

Send resume to David Kellogg at dkellogg@judge.com

Job Summary:
The Senior Data & Analytics Engineer leads the transformation of a legacy data platform into a modern, cloud‑based analytics ecosystem within an asset management or fund services environment. This role is responsible for modernizing complex data pipelines while maintaining continuity for existing reporting and downstream consumers. The position requires deep experience across investment data domains, multi‑asset portfolios, and large‑scale data engineering platforms.

Key Responsibilities:

• Own the migration from legacy data pipelines, stored procedures, and spreadsheets to a modern cloud data platform
• Design and build trusted, well‑modeled data layers that support firm‑wide reporting and analytics
• Re‑implement existing business logic in tested, version‑controlled, and documented code
• Reconcile data across multiple source systems and establish enterprise data quality standards
• Define and enforce pragmatic data quality practices, including fix‑at‑source, downstream remediation, or documented exceptions
• Maintain continuity of existing reporting and analytics throughout the transformation
• Partner with investment, operations, and technology teams to ensure consistent data definitions and usage

Required Experience:

• 7–10+ years of experience in data engineering or analytics engineering
• Minimum 3–5 years of experience in asset management, fund administration, buy‑side, custodian, or investment services environments
• Proven experience leading or owning a data platform modernization initiative
• Experience working with complex legacy systems and multiple data sources

Technical Skills:

• Python and SQL
• Snowflake
• Airflow or equivalent orchestration tools
• Cloud data platforms, including storage, orchestration, and serverless services
• Data modeling techniques (dimensional modeling, SCDs, snapshots)
• Data quality frameworks (dbt tests, Great Expectations, data contracts)
• CI/CD practices for data pipelines
• Migration of stored procedures to modern ELT architectures

Investment Data Domain Expertise:

• Investment data lifecycle: positions, transactions, pricing, benchmarks, corporate actions, and cash flows
• Multi‑asset class exposure including equities, fixed income, derivatives, and alternatives
• Portfolio analytics including returns, attribution, and risk metrics
• Risk systems experience preferred (Aladdin, RiskMetrics, Barra, or equivalent)
• Middle and back‑office systems such as OMS, portfolio accounting platforms, and custodian feeds
• Financial data vendors including Bloomberg, MSCI, and FactSet
• Understanding of IBOR versus ABOR data models

Preferred Qualifications:

• Experience in enterprise‑scale data platforms supporting institutional investment firms
• Strong ability to balance data correctness, timeliness, and practicality during platform transitions

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