Lead Data Engineer

Harbor Capital Advisors•Chicago, IL

12h

About The Position

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

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.)
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

Nice To Haves

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
Financial services experience is a plus but not required

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