Lead Data Engineer

Harbor Capital Advisors•Chicago, IL

1d•Hybrid

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. The role combines hands-on engineering with technical leadership, architectural decision-making, and cross-functional influence. The Lead Data Engineer will develop end-to-end data ecosystems—including pipelines, storage, APIs, and AI-enabled services—using modern cloud infrastructure and emerging AI/LLM capabilities. The work supports investment decisions and client outcomes by turning data into actionable insights and production-grade tools. The role also includes mentoring engineers, setting technical standards, and guiding the evolution of the data platform to ensure scalability, reliability, and alignment with long-term business strategy. The Lead Data Engineer will partner with teams across Multi-Asset Solutions, Investment Research, Investment Products, Accounting, Marketing, and Distribution to translate business needs into robust data solutions. This is a hybrid opportunity based in the Chicago office.

Requirements

Deep expertise in data engineering fundamentals, including Python and SQL, with production experience.
Proven experience designing scalable, reliable data architectures and pipelines.
Strong experience with the AWS cloud ecosystem, including infrastructure, CI/CD, and observability.
Hands-on experience with AI-enabled applications and data pipelines.
Strong experience with Snowflake, including Cortex AI, or similar platforms.
Expertise in DevOps, Docker/containerization, and CI/CD pipelines.
Strong knowledge of database systems such as Postgres or Aurora, or similar.
Experience with Infrastructure as Code such as CloudFormation or Pulumi; Terraform is also mentioned as a plus.
Experience integrating and leveraging LLM developer tools such as Claude Code or Copilot.
Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field; quantitative disciplines are also welcome.
10+ years of experience in Data Engineering, Software Engineering, or related fields.
2–4+ years of experience leading projects or teams, formally or informally.
Experience delivering production-grade data platforms or AI-enabled systems.
Ability to apply emerging AI/LLM capabilities pragmatically, with curiosity and good judgment.
Comfort with ambiguity and evolving requirements and technical approaches; ability to take ownership of systems, standards, and outcomes.

Nice To Haves

Experience leading or mentoring engineering teams.
Experience in financial services or regulated environments; financial services experience is a plus but not required.
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.
Experience with Terraform.

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.
Prototype, test, iterate, and validate solutions before scaling.
Drive work through production and subsequent refinement.
Communicate openly, contribute to design discussions, and challenge assumptions constructively.