Senior Data Scientist/Data Engineer - Quantitative Risk & Unstructured Market Intelligence Pipeline

We are an established quantitative asset management firm upgrading our internal portfolio monitoring and market intelligence infrastructure. We process both structured market risk metrics and unstructured narrative data (such as earnings call transcripts, analyst notes, and regulatory filings) We are seeking a senior-level Data Scientist / Data Engineer for a highly targeted technical advisory and architectural design engagement. The goal is to audit our cross-cloud data ingestion architecture and design a unified data model that feeds both our quantitative risk dashboards and our enterprise AI productivity tools. Our quantitative data sits in an AWS environment, while our corporate operations, reporting, and business intelligence are hosted on Microsoft Azure and Microsoft 365. Currently, we struggle to cleanly unify: 1. Structured Risk Data: Historical volatility, asset correlation matrices, and portfolio exposure metrics stored in PostgreSQL. 2. Unstructured Market Intelligence: Daily market reports and regulatory filings parsed using Python ETL pipelines. We need a cohesive data modeling strategy that structures these heterogeneous datasets so they can be seamlessly consumed by Microsoft Power BI for interactive executive risk dashboards, and securely indexed into Microsoft 365 Copilot to enable our analysts to query real-time market intelligence using natural language within their daily workflow. Phase 1: Hybrid-Cloud Data Modeling & ETL Audit - Review our Python-based ETL pipelines that extract and clean unstructured text and numerical risk data. - Design a optimized PostgreSQL data model capable of handling relational financial metrics alongside text-based metadata. - Provide architectural recommendations for securely bridging our data pipelines between AWS (where raw feeds land) and Microsoft Azure (where downstream analytics reside). Phase 2: BI & Enterprise AI Integration Blueprint - Map out the integration path between PostgreSQL and Microsoft Power BI, ensuring optimized query performance for real-time risk visualization. - Outline the strategy to connect our processed market intelligence data to Microsoft 365 Copilot (using Azure AI Search connectors or Copilot Studio), allowing internal stakeholders to securely search and summarize proprietary portfolio risk insights directly from Teams, Outlook, or Word. We are looking for an experienced Data Engineer/Data Scientist who bridges the gap between deep technical pipelines and business intelligence. You should have a strong grasp of financial data modeling and practical experience working in hybrid AWS/Azure environments. You also need to understand how to prepare data not just for traditional relational databases, but also for modern semantic search and enterprise AI agents.