Data Engineer Snowflake,DBT, Asset Management

Job Description
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Lead Data Engineer (Snowflake/dbt) | Asset Management

• Location: London (Hybrid - 3 days/week in office)
• Duration: 6-12 Months (Initial)
• Rate: Negotiable
• Keywords: Snowflake, dbt, Asset Management, Aladdin, ETL, ELT, Data Engineering

Role Summary

We are seeking a high-caliber Lead Data Engineer to drive a brand-new, strategic data transformation for a Tier-1 Global Asset Manager. Working as a senior technical consultant, you will bridge the gap between "engine-room" hands-on development and "boardroom" strategic advisory.

This is a greenfield-standard project where you will review existing engineering standards, critique the brand-new data plan, and implement a best-in-class Snowflake and dbt ecosystem from the ground up.

Technical Requirements: Snowflake & dbt Engineering

We are looking for Engineering-level depth. You must apply software engineering rigor (CI/CD, modularity, testing) to data transformation.

1. Snowflake Mastery

• Architecture: Advanced knowledge of Snowflake internals (Micro-partitions, Clustering, Query Profiling) for high-performance financial data processing.
• Lifecycle Management: Expert use of Zero-Copy Cloning, Time Travel, and Fail-safe for robust dev/test workflows.
• Governance: Implementation of RBAC, Data Masking, and Resource Monitors to balance regulatory compliance with cost-efficiency.

2. dbt (Data Build Tool) Engineering

• Modular Architecture: Designing scalable dbt models (Staging Intermediate Marts) to create a definitive "Single Source of Truth."
• Advanced Jinja/Macros: Leveraging Jinja to automate SQL patterns and manage complex, environment-specific logic.
• Data Integrity: Building rigorous testing frameworks (Schema, Data, and custom tests) to ensure 100% accuracy for executive reporting.
• CI/CD: Treating "Data as Code" by integrating dbt into modern DevOps pipelines.

Domain & Strategic Profile

• Asset Management Expertise: You must have "through-and-through" experience in the investment space (Trade lifecycles, Portfolio Construction, Risk).
• Aladdin Integration: Ideally, you have worked with Aladdin Data Cloud (ADC) or modeled complex data exports from the Aladdin platform.
• Leadership & Engagement: Very involved with client Architects and Tech Leadership. You must be comfortable presenting progress, technical roadmaps, and architectural shifts to senior stakeholders.
• Methodology: Expert understanding of modern ETL/ELT methodologies and a desire to set new engineering standards.

Logistics & "The Fit"

• Location: Ideally London-based. We will consider candidates from the North of England who are committed to traveling down for 2 days per week in-office.
• Onboarding: 3-4 weeks total lead time (includes a 2-week dedicated client-onboarding phase).
• The Process: Initial competency check followed by a "fit-check" and introduction to the end-client leadership. xkybehq
• Flexibility: For a candidate with the specific Snowflake + dbt + Aladdin profile, terms and location arrangements are a negotiation.