Platform Integration Engineer – Remote & Cloud Data

Huron; Chicago (IL); Hybrid; USD 100,000 - 130,000; Full time

Benefits offered by this job

Flexible remote-first or hybrid work

Job summary

Huron is seeking a Platform Integration Engineer to unify a diverse analytics ecosystem into a scalable technology platform. You will integrate applications, design APIs and event streams, and build shared services for authentication, observability, and logging.

You will work with data pipelines across distributed teams, surface data from Spark and relational databases, and collaborate with domain teams to unify data semantics while advocating reliable, scalable architecture.

Qualifications

• 3+ years of software engineering experience with integration, platform, or infrastructure work.
• Proficiency in at least one backend language (Python, Go, Java) and experience with two+ languages preferred.
• Hands-on experience with cloud platforms (AWS strongly preferred).
• Experience building and operating data pipelines (ETL/ELT) with schema management and data quality monitoring.
• Experience with AI-assisted development tooling in a professional setting.
• Strong written communication skills for ADRs, runbooks, and design docs.

Responsibilities

• Audit analytics applications to understand tech choices, data flows, and integration points.
• Design and implement integration patterns (APIs, event streams, data contracts).
• Build and maintain platform services like authentication, observability, and logging.
• Develop unified data presentation layers across heterogeneous compute environments.
• Collaborate with domain teams to identify unification opportunities while respecting data semantics.

Skills

Python

Go

Java

AWS

Data pipelines

AI-assisted tooling

Tools

Airflow

dbt

MLflow

Spark

Job description

Huron is seeking a Platform Integration Engineer to unify a diverse analytics ecosystem into a scalable technology platform. You will integrate applications, design APIs and event streams, and build shared services for authentication, observability, and logging.

You will work with data pipelines across distributed teams, surface data from Spark and relational databases, and collaborate with domain teams to unify data semantics while advocating reliable, scalable architecture.