Lead Data Engineer

Role Description

We are seeking a Lead Data Engineer to drive the design, architecture, and delivery of scalable data platforms, AI-powered data products, and enterprise-grade data services. This role goes beyond hands-on engineering to include technical leadership, architectural decision-making, and cross-functional influence across the organization.

You will lead the development of end-to-end data ecosystems —including data pipelines, storage, APIs, and AI-enabled services—leveraging modern cloud infrastructure and emerging AI/LLM capabilities. You will play a critical role in shaping how data is transformed into actionable insights and production-grade tools that directly support investment decisions and client outcomes.

In addition to building, you will mentor engineers, set technical standards, and guide the evolution of our data platform , ensuring scalability, reliability, and alignment with long-term business strategy.

You will partner closely with teams across Multi-Asset Solutions, Investment Research, Investment Products, Accounting, Marketing, and Distribution to translate complex business needs into robust, high-impact data solutions.

Why would you want to work on our team?

You will operate at the intersection of data engineering, AI innovation, and investment decision-making, with a high degree of ownership and visibility. This is a role where you will:

• Influence technical direction and architecture of a growing data platform
• Build AI-powered data products used directly by investors and business leaders
• Work in a lean, high-impact team with minimal bureaucracy
• Have a clear line of sight between your work and real business outcomes
• Help define best practices in a modern, cloud-native, AI-enabled environment

Who will thrive in this role?

This role is ideal for engineers who:

• Think beyond implementation and design systems and platforms
• Enjoy mentoring others and leading technical discussions
• Balance hands-on coding with strategic thinking
• Are comfortable navigating ambiguity and shaping solutions from early-stage ideas
• Take ownership not just of features, but of systems, standards, and outcomes

Key Responsibilities

• Lead the architecture, design, and implementation of scalable data platforms and AI-enabled data products
• Define and enforce data engineering standards, best practices, and design patterns
• Own the end-to-end lifecycle of data pipelines, data services, and APIs from concept through production
• Drive adoption of AI/LLM capabilities within data workflows and products
• Partner with business stakeholders to translate complex requirements into technical solutions
• Mentor and guide engineers, providing technical leadership and code reviews
• Improve system reliability, observability, and performance across the data stack
• Lead technical decision-making on tools, frameworks, and architecture
• Ensure data governance, security, and compliance standards are embedded into systems
• Collaborate across teams and contribute across the stack when needed

Key Behavioral Expectations

• Applies emerging AI/LLM capabilities pragmatically, with curiosity and good judgment
• Comfortable with discovery work: prototype, test, iterate, and validate before scaling
• Adapts quickly as requirements and technical approaches evolve
• Owns delivery end-to-end, driving work through production and refinement
• Communicates openly, contributes to design discussions, and challenges assumptions constructively
• Collaborates across teams and works across the stack when needed

Technical Knowledge, Skills & Abilities

Core Requirements:

• Deep expertise in data engineering fundamentals (Python, SQL) with production experience
• Proven experience designing scalable, reliable data architectures and pipelines
• Strong experience with AWS cloud ecosystem (infrastructure, CI/CD, observability)
• Hands-on experience with AI-enabled applications and data pipelines
• Strong experience with Snowflake (including Cortex AI) or similar platforms
• Expertise in DevOps, containerization (Docker), and CI/CD pipelines
• Strong knowledge of database systems (Postgres, Aurora, or similar)
• Experience with Infrastructure as Code (CloudFormation, Pulumi; Terraform a plus)
• Experience integrating and leveraging LLM developer tools (Claude Code, Copilot, etc.)

Nice to Have:

• Experience leading or mentoring engineering teams
• Experience in financial services or regulated environments
• Knowledge of data governance, lineage, and security frameworks
• Experience designing data products or internal platforms
• Familiarity with front-end or full-stack development for end-to-end ownership

Educational Qualifications & Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related field (quantitative disciplines also welcome)
• 10+ years of experience in Data Engineering, Software Engineering, or related fields
• 2–4+ years of experience leading projects or teams (formal or informal leadership)
• Experience delivering production-grade data platforms or AI-enabled systems
• Financial services experience is a plus but not required

Compensation Pay Range: This position offers a competitive base salary range of $180,000–$220,000, commensurate with experience and qualifications.

This position is a hybrid opportunity based in our Chicago office. #LI-Hybrid