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# Lead Data Platform Engineer (Manager / Architect)

- **Company:** Huron Consulting Group Inc.
- **Location:** Chicago, IL, United States (Remote available)
- **Experience:** Expert
- **Salary:** $140,000.0 - $190,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Apache HTTP Server, Microsoft Azure, Big Data, Software as a Service, Cloud Computing, Configuration Management, Code Review, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Sharing, Database Queries, Distributed Systems, IT Management, JSON, Python (Programming Language), Azure Machine Learning, Software Engineering, SQL Databases, Data Streaming, Parquet, Data Logging, Delivery Pipeline, Snowflake, Apache Spark, AI Coding Agents, Infrastructure Automation Frameworks, Avro, Production Code, Machine Learning Operations, Terraform, Data Pipelines
- **Published:** October 22, 2026
- **Apply:** https://www.chicagocareersite.com/job.asp?id=3472156765&tx=JJ3125FFG&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 7+ years of software engineering experience, with at least 3 years focused on data engineering, integration, platform, or infrastructure work
 * 2+ years in a technical lead role: setting direction for shared work, mentoring engineers, and reviewing code and designs
 * Proficiency in Python; comfortable reading and writing production-quality code, not just scripts
 * Strong SQL skills, including working with large or complex datasets
 * Hands-on experience with AWS (compute, storage, and at least one managed data service)
 * Experience with AI-assisted development tools or coding agents in a professional context, and a genuine curiosity about how AI is changing engineering work
 * Demonstrated ability to learn unfamiliar systems quickly: you can walk into an existing pipeline, tool, or codebase you didn't build and figure out how it works
 * Strong written communication skills, * 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 data pipelines (ETL/ELT), including schema management or data quality monitoring
 * Exposure to structured (SQL) and semi-structured (JSON, Parquet, Avro) data at scale
 * Familiarity with analytics or ML platform tooling (Airflow, MLflow, or SaaS equivalents)

## Description

This role is for an engineer who is energized by ambiguity, skilled at reading distributed systems, and opinionated (but flexible) about platform design. You will work directly with engineering leads and data teams. As the team's technical lead, you will guide and mentor other engineers while remaining a hands-on contributor.

What You'll Do

Technical Leadership

 * Set technical direction for a small team of platform engineers, translating requirements and architecture guidance into well-scoped, sequenced work
 * Mentor engineers through pairing, code review, and design feedback, raising the team's standards for quality, testing, and documentation
 * Coordinate delivery across contributors, including distributed team members, surfacing risks and dependencies early
 * Remain a primary hands-on contributor to the team's most complex integration and platform work

Platform Unification & Integration

 * Audit existing analytics applications to understand their underlying technology choices, data flows, and integration points
 * Design and implement integration patterns (APIs, event streams, shared data contracts) that allow siloed applications to interoperate without unnecessary rewrites
 * Build and maintain shared platform services (authentication, observability, configuration management, logging) that reduce duplication across applications

Data Pipeline & ETL Engineering

 * Develop a deep understanding of existing data pipelines owned by distributed teams, including their data sources, transformation logic, and dependencies
 * Partner with domain teams who have the deepest knowledge of their own data, acting as an integration architect and collaborator rather than a centralized pipeline owner; identify unification opportunities but defer to domain expertise on data semantics and processing logic

Cloud Infrastructure

 * Deploy and manage platform services on cloud infrastructure (AWS preferred; Azure/GCP experience a plus)
 * 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, translating technical requirements into business value, risk reduction, and compliance language that resonates with IT governance and procurement stakeholders