P

Picton Mahoney Asset Management

Alternative investment management with hedging strategies

Vice President - Data Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeToronto, ON, CanadaHybrid

About the Job

Requirements

  • At least 10 years of experience in data engineering, data platforms, architecture, or technology leadership, including significant team leadership experience.
  • Experience owning data across multiple business lines or functions, balancing competing needs, and driving shared standards.
  • Experience building agentic AI solutions, including designing agents and workflows that use tools, retrieve enterprise data, operate within security and governance boundaries, and have practical approaches to evaluating accuracy and reliability.
  • Strong experience with modern cloud data platforms, particularly Databricks, Snowflake, SQL, APIs, and distributed data pipelines.
  • Deep understanding of financial and investment data, including securities, portfolios, market data, performance, risk, and time-series data.
  • Experience building data platforms that support analytics, applications, and AI/LLM workloads, including retrieval, semantic layers, and structured data access for models.
  • Strong understanding of data governance, quality, lineage, security, and ownership, including how these extend to AI systems.
  • Ability to move between strategic thinking and technical detail and make pragmatic architecture decisions.
  • Strong business acumen and stakeholder-management skills.
  • Current hands-on use of AI in the candidate’s workflow, using tools such as Claude Code, Cursor, GitHub Copilot, or similar to build, debug, document, and analyze, with concrete examples of impact on speed or output.
  • Experience with LLM evaluation, observability, or AI cost management in production, and familiarity with Model Context Protocol (MCP) or similar standards for connecting AI to enterprise systems.
  • A track record of mentoring engineers and shaping team culture, including examples of developing people and helping teams take on more ownership and improve how they work.

Responsibilities

  • Serve as the single accountable owner for data across the company’s businesses, setting direction, resolving competing priorities, and ensuring each business can rely on consistent, trusted data.
  • Define and execute the enterprise data strategy, architecture, and roadmap across Databricks, Snowflake, APIs, applications, analytics, and AI.
  • Own the technical build that enables the AI roadmap, working with AI and business leaders to sequence data capabilities so planned AI initiatives have the data, access, and governance needed to ship.
  • Design the platform so AI is a first-class consumer of data, including well-governed semantic layers, rich metadata, and secure interfaces such as APIs and MCP servers for agents and LLM applications.
  • Make pragmatic architecture decisions balancing performance, security, cost, and business needs, including AI workload cost and governance.
  • Build a modern data platform supporting portfolio construction, performance, risk, analytics, and AI use cases.
  • Lead the design of reliable, scalable pipelines for market, security, portfolio, and historical data, using AI coding assistants and agentic tools to shorten build and iteration cycles.
  • Apply AI to the data lifecycle, including automated data quality checks, anomaly detection, lineage and documentation generation, metadata enrichment, and reconciliation.
  • Establish practical standards for data quality, lineage, governance, security, and ownership, including guardrails for AI tools and agents accessing sensitive financial data.
  • Evolve financial data models and semantic layers so data is consistent and reusable across products, teams, and AI systems.
  • Build and lead a high-performing Data Engineering and Data Platform team.
  • Partner with Engineering, Investment, Product, and AI leaders to translate business needs into effective data solutions.
  • Identify opportunities to use modern data and AI technologies to create competitive advantage.
  • Communicate complex technical concepts effectively to technical and business stakeholders.

About the Company

Picton Mahoney Asset Management

Alternative investment management with hedging strategies

This company provides Canadian investors with access to alternative investment strategies through mutual funds and other investment solutions. It focuses on risk management and diversification by using deep quantitative research, fundamental analysis, and hedging techniques to build portfolios that aim to perform well across different market conditions, especially during downturns. The firm emphasizes a philosophy of building portfolios that can withstand bear markets and deliver more consistent, risk-adjusted returns by reducing correlation with traditional asset classes like stocks and bonds. The goal is to offer sophisticated strategies to advisors, individuals, and institutions through a range of investment products and advisory services.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
Canada
Founded
Not listed

Recently Posted Jobs

Explore more opportunities at Picton Mahoney Asset Management

See more jobs