Director - Data & Systems Engineering
Overview
We are seeking a hands-on Director of Data & Systems Engineering to lead our Data Engineering and Software Engineering functions. This player-coach leader will own the architecture, delivery, and operational excellence of enterprise data platforms, APIs, backend systems, and business-critical applications supporting trading, risk management, asset management, analytics, and operations.
The ideal candidate combines strong technical leadership with deep expertise in modern data platforms, distributed systems, and cloud-native engineering. While not an AI-focused role, this leader will partner closely with AI teams to ensure platforms are optimized for AI-enabled use cases.
Key Responsibilities
Lead and mentor a team of engineers across Data, Software, API, and Platform Engineering.
Define technology strategy, architecture standards, and engineering best practices.
Drive development and modernization of trading, asset management, and enterprise platforms.
Own enterprise data strategy, including Snowflake architecture, data pipelines, governance, quality, and performance optimization.
Oversee delivery of APIs, backend services, integrations, and internal applications.
Partner with business stakeholders to translate requirements into scalable technology solutions.
Ensure platform reliability, security, observability, CI/CD maturity, and operational excellence.
Collaborate with AI teams to support RAG, semantic search, vector databases, model-facing APIs, and responsible AI initiatives.
Required Qualifications
Bachelor's degree in Computer Science, Engineering, or related field.
15+ years of experience in Software Engineering, Data Engineering, Platform Engineering, or Architecture.
7+ years of leadership experience managing engineering teams.
Strong hands-on expertise in Python, Snowflake, Databricks, FastAPI, REST APIs, PostgreSQL, Redis, Azure, and distributed systems.
Experience designing and operating large-scale data platforms, APIs, and cloud-native applications.
Strong knowledge of DevOps, CI/CD, observability, testing, and production operations.
Working knowledge of AI/LLM technologies, including RAG, embeddings, vector databases, and model integration.
Excellent communication skills with the ability to engage technical and executive stakeholders.
Preferred
Experience in energy trading, commodities, financial services, or other data-intensive industries.
Exposure to Snowflake Cortex, Azure OpenAI, Azure AI Services, LangGraph, Semantic Kernel, MCP, or similar modern AI/data platforms.
Technology Stack: Python, Snowflake, Databricks, FastAPI, PostgreSQL, Redis, Azure, APIs, Distributed Systems, AI/LLM Platforms.
FAQs
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