Overview
As a Senior Data Engineer, you will be a technical cornerstone of the data platform team. You will be responsible for the end-to-end engineering of data pipelines, from ingestion to transformation, ensuring that the Snowflake warehouse is optimized for complex wealth management analytics. You will work at the intersection of sophisticated financial data and enterprise-grade engineering. Client: Our client is a prominent global wealth and asset management firm focused on delivering innovative investment solutions and a networked ecosystem of international opportunities. The organization operates within a highly regulated environment, prioritizing the transformation of complex concepts into actionable financial strategies for a global clientele. Project Overview: The project involves the development and expansion of a centralized, scalable data ecosystem. Having successfully ingested the initial data source, the team is now moving into a high-growth phase to integrate multiple high-value financial and operational data streams. The goal is to provide C-level executives and advisors with a unified, reliable reporting layer for strategic decision-making.
- Build and maintain scalable data pipelines for ingestion into Snowflake.
- Develop and manage transformations using dbt, including modeling, testing, and comprehensive documentation.
- Design and orchestrate complex data workflows using Prefect.
- Utilize Workato to integrate and normalize data from critical SaaS platforms, including Addepar, NetSuite, Salesforce, Morningstar, and UKG.
- Design specialized data models tailored for portfolio, client, and financial reporting.
- Ensure data quality, lineage, and governance best practices while optimizing performance and costs within Snowflake.
- Partner with Tableau developers and business stakeholders to align technical delivery with reporting needs.
- 5+ years of experience in data engineering with a focus on building modern data platforms.
- Hands-on experience with Snowflake and dbt, supported by strong SQL skills and advanced data modeling expertise.
- Proven familiarity with ETL/ELT patterns and API-based ingestion.
- Practical experience with orchestration tools such as Prefect or Airflow.
- A solid understanding of financial or wealth management data structures.
- Experience supporting reporting requirements for Tableau-based visualization layers.
- Experience within the Wealth Management or Investment Management sector, with specific familiarity handling investment and portfolio-related data.
- Understanding of regulatory requirements regarding financial data handling and privacy.
- Background in integrating market data providers such as Bloomberg or Morningstar.
- Advanced SQL skills and experience with Python for data engineering tasks.
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