Data Engineer

Description

We are looking for an experienced Data Engineer to join a construction and contractor-focused organization in Appleton, Wisconsin. This contract opportunity with potential for a permanent role is ideal for a senior-level candidate who enjoys building scalable cloud-based data platforms, working hands-on with Python and notebook-driven development, and applying AI-enabled tools to create practical business solutions. The role will focus on designing modern data lake capabilities, improving data movement and transformation processes, and partnering with stakeholders to deliver reliable analytics infrastructure.

Responsibilities

Design, build, and enhance modern data lake architecture in Google Cloud Platform to support scalable and efficient data operations.

Develop robust data pipelines using Python, SQL, Spark, and ETL frameworks to ingest, transform, and prepare data from multiple sources.

Create and maintain notebook-based solutions that demonstrate clear technical approaches, reusable logic, and well-documented project outcomes.

Integrate large-scale data processing technologies such as Hadoop and Kafka to support high-volume and streaming data workloads.

Collaborate with cross-functional teams to translate business needs into data engineering solutions that improve reporting, analytics, and operational decision-making.

Apply AI-driven tools and approaches to accelerate development, improve solution quality, and deliver innovative customer-focused outcomes.

Support cloud data environments that may include Azure Data Lake and related platforms as part of broader enterprise data initiatives.

Contribute to data platform improvements, including work connected to enterprise tool adoption or internal platform changes when needed.

Requirements

10+ years of experience in data engineering or closely related data platform roles.

Strong hands-on expertise with Google Cloud Platform and building modern data lake solutions in cloud environments.

Advanced programming ability in Python, including experience creating practical notebook-based projects that can be reviewed and discussed.

Proven background working with Apache Spark, Hadoop, Kafka, SQL, and ETL processes in production settings.

Demonstrated ability to design scalable data pipelines and manage large, complex datasets across distributed systems.

Genuine interest in AI and a track record of using AI-enabled tools or methods to improve technical delivery.

Experience communicating technical concepts clearly and partnering effectively with both engineering teams and business stakeholders.

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