Data Engineer

Largeton Group; Woodbury (MN); Hybrid; USD 83,000 - 124,000; Full time

Job summary

Largeton Group in Woodbury, MN is seeking a hands-on Data Engineer to accelerate a major analytics initiative. You will design and build scalable data pipelines, transform and model data with DBT and PySpark, and expose data via APIs for customer-facing analytics.

The role emphasizes on-prem data environments, onsite work with some flexibility, and collaboration with a small team to deliver an analytics platform by year-end.

Qualifications

• 4+ years of data engineering experience.
• Strong SQL and Python skills.
• Experience developing APIs or API-driven data delivery.

Responsibilities

• Design, build, and maintain scalable data pipelines and integration processes.
• Develop and manage data transformation workflows using DBT.
• Build and optimize ETL/ELT processes with PySpark, Airflow, and Python.
• Ingest, process, and deliver data in an on-premises environment.
• Expose customer data through APIs for consumption in BI tools.
• Support analytics visualization with semantic layers and tools.

Tools

DBT

PySpark

Apache Airflow

Python

Job description

Title: Data Engineer

Location: Woodbury, MN (Primarily Onsite 5 days/week with Some Flexibility)

Duration: Initial 3-Month Contract with Potential Month-to-Month Extensions

Must have skills: DBT, Spark, Airflow, Python

Overview

Our client is seeking a Data Engineer to help accelerate a major analytics initiative with the goal of delivering a customer-facing analytics platform by year-end. These consultants will be responsible for building scalable data pipelines, transforming and modeling data, exposing data through APIs, and enabling rich analytics experiences for customers. The team is focused on creating a seamless way for customers to access, analyze, and consume their data, whether through the company's portal or their own BI tools. These individuals will collaborate with a team of approximately five other professionals and play a key role in building the data foundation that powers the user experience.

Responsibilities

• Design, build, and maintain scalable data pipelines and integration processes.
• Develop and manage data transformation workflows using DBT.
• Build and optimize ETL/ELT processes utilizing PySpark, Airflow, and Python.
• Work within an on-premises environment to ingest, process, and deliver data efficiently.
• Expose customer data through APIs, enabling customers to consume data within their own BI and analytics platforms.
• Support the development of highly visual analytics experiences through semantic and visualization layers such as Cube.dev or similar technologies.
• Partner closely with developers responsible for building customer-facing applications and dashboards.
• Leverage AI tools, including Claude, to improve development efficiency and accelerate delivery.
• Collaborate with business and technical stakeholders to ensure solutions align with product objectives and customer needs.
• Contribute to the architecture and strategy of a modern analytics platform.

Required Qualifications

• 4+ years of Data Engineering experience.
• Strong experience with DBT, PySpark, and Apache Airflow.
• Experience building scalable data pipelines and transformation frameworks.
• Strong SQL and Python skills.
• Experience developing APIs or supporting API-driven data delivery.
• Experience working within on-premises data environments.
• Ability to work independently while collaborating effectively within a team environment.

Preferred Qualifications

• Experience with Cube.dev or similar semantic layer and analytics visualization technologies.
• Exposure to AI-assisted development tools, particularly Claude.
• Experience supporting customer-facing analytics, reporting, or data product initiatives.
• Familiarity with modern data architecture and best practices.

Ideal Candidate

The ideal candidate is a hands-on Data Engineer who enjoys building modern data platforms, transforming complex data into meaningful insights, and enabling customers to access and visualize their data in intuitive ways. This person will be comfortable working across data engineering, API development, analytics enablement, and emerging AI technologies in a collaborative, fast-moving environment.