Data Engineer

Title: Data Architect / Portfolio Systems Designer
Function: Canonical Data Modelling & Portfolio Data Architecture
Department: Engineering / Product
Reports To: Product & Data Engineering Leadership

Role Overview

We are seeking a hybrid data architect product-minded engineer responsible for defining the canonical data model and truth layer of our trading and portfolio systems. This person will design core schemas validate data flows prototype mappings and collaborate with product PMs and engineering to ensure the system has accurate coherent end-to-end portfolio data.

Key Responsibilities

• Design canonical data models for accounts positions transactions tax lots and the security master
  

• Define keys identifiers lineage and data contracts across systems
  

• Prototype mappings using SQL and Python to validate assumptions
  

• Analyze custodial market data feeds (BridgeFT FIX pricing Greeks)
  

• Validate correctness of reconciliations P&L tax lots and option linkages
  

• Document entity definitions data flows lineage and field-level specifications
  

• Collaborate with Product portfolio managers and backend engineers
  

• Shape architectural decisions on batch vs. real-time flows snapshotting and identifier strategy
  

• Own the correctness and consistency of the portfolio data layer
  

Required Skills & Experience

• Strong data modeling and systems thinking
  

• Deep financial/portfolio domain knowledge (positions tax lots P&L options FIX)
  

• Hands-on SQL Python for prototyping
  

• Experience with financial or time-series-heavy systems
  

• Ability to design schemas for multi-custodial environments
  

• Strong documentation and data contract creation skills
  

• 47 years backend or data engineering experience
  

Preferred Experience

• OMS/EMS/portfolio system experience
  

• Options and derivatives modeling
  

• FIX protocol integration
  

• Multi-custodial data experience (BridgeFT Black Diamond Orion)
  

• Experience with Python FastAPI microservices real-time pipelines
  

What Success Looks Like

• Clear unified canonical data model
  

• Correct and complete security master identifier strategy
  

• Accurate multi-custodial mapping of accounts and positions
  

• Clean tax lot position lifecycle
  

• Fully documented data flows engineers can implement
  

• Reduction of ambiguity fragmentation or duplication in data