Lead Data Platform Engineer (Manager / Architect)

Huron Consulting Services•Chicago, IL

1d•$140,000 - $190,000•Remote

About The Position

Huron is seeking a Lead Data Platform Engineer to provide technical leadership and hands-on engineering for a platform team working to unify a diverse ecosystem of analytics applications. The role involves assessing existing tools and systems, choosing when to consolidate versus preserve distinct technologies, and building scalable integration patterns and shared platform services. The engineer will work directly with engineering leads and data teams, guide and mentor other engineers, and contribute to complex integration and platform work. The position also focuses on understanding data pipelines owned by distributed teams and collaborating with domain experts on integration opportunities while respecting their ownership of data semantics and processing logic. The engineer will deploy and manage cloud platform services, contribute to infrastructure-as-code practices, collaborate on deployment and reliability standards, and develop evidence-based business cases for cloud feature adoption. The role is remote and based in the United States. The estimated base salary is $140,000–$190,000, with an estimated total compensation range including annual incentive compensation of $168,000–$228,000. The position is eligible for Huron benefit plans.

Requirements

At least 7 years of software engineering experience, including at least 3 years focused on data engineering, integration, platform, or infrastructure work.
At least 2 years in a technical lead role involving setting direction for shared work, mentoring engineers, and reviewing code and designs.
Proficiency in Python and ability to write and read production-quality code.
Strong SQL skills, including experience with large or complex datasets.
Hands-on AWS experience with compute, storage, and at least one managed data service.
Professional experience with AI-assisted development tools or coding agents and genuine curiosity about AI’s impact on engineering work.
Ability to quickly understand unfamiliar systems, pipelines, tools, or codebases.
Strong written communication skills.

Nice To Haves

Experience with dbt and Snowflake.
Experience with Apache Iceberg.
Additional language proficiency, especially Spark or Java.
Experience leading or coordinating engineering work across distributed or multi-location teams.
Experience with ETL/ELT data pipelines, including schema management or data quality monitoring.
Exposure to structured SQL and semi-structured JSON, Parquet, or Avro data at scale.
Familiarity with analytics or ML platform tooling such as Airflow, MLflow, or SaaS equivalents.
Azure or GCP experience.

Responsibilities

Set technical direction for a small team of platform engineers and translate requirements and architecture guidance into sequenced, well-scoped work.
Mentor engineers through pairing, code reviews, and design feedback, improving standards for quality, testing, and documentation.
Coordinate delivery among contributors, including distributed team members, and surface risks and dependencies early.
Remain a hands-on contributor to complex integration and platform work.
Audit analytics applications to understand technology choices, data flows, and integration points.
Design and implement integration patterns, including APIs, event streams, and shared data contracts, to connect siloed applications without unnecessary rewrites.
Build and maintain shared platform services for authentication, observability, configuration management, and logging.
Develop an understanding of data pipelines owned by distributed teams, including their sources, transformation logic, and dependencies.
Collaborate with domain teams as an integration architect, identify unification opportunities, and defer to domain expertise on data semantics and processing logic.
Deploy and manage platform services on cloud infrastructure.
Contribute to infrastructure-as-code practices using tools such as Terraform or CDK.
Collaborate with the IT platform team to define deployment pipelines and reliability standards for integration services.
Construct evidence-based business cases for cloud feature adoption, expressing technical requirements in terms of business value, risk reduction, and compliance.