Data Engineer - ONSITE

Network Distribution•Schaumburg, IL

1d•$100,000 - $130,000•Onsite

About The Position

The Data Engineer designs, builds, and maintains scalable data pipelines for Network Distribution’s enterprise data and AI platforms. This role will ingest, transform, and validate data from operational systems into reliable, business-ready datasets that support reporting, analytics, operational decision-making, and Machine Learning initiatives. The Data Engineer will collaborate with IT, BI developers, business stakeholders, and project teams, the Data Engineer develops reusable pipelines and data models while ensuring data quality, security, and performance. Success requires strong SQL and Python skills, experience with cloud data platforms, an understanding of modern data engineering and Machine Learning concepts, and a basic understanding of supply chain data such as orders, inventory, logistics, and financial reporting are essential.

Requirements

Strong SQL development, query optimization, data transformation, and ETL/ELT pipeline development experience.
Experience with Snowflake, including data warehousing, data modeling, performance optimization, and cloud-based data processing.
Experience with cloud platforms and services such as AWS.
Python development and scripting experience.
Experience with APIs, data ingestion, integration technologies, and structured and semi-structured data, including JSON and XML.
Understanding of data warehouse architecture, data modeling, data validation, monitoring, troubleshooting, and performance optimization.
Working knowledge of Machine Learning concepts and experience preparing and transforming data to support ML models and AI/ML workloads.
Familiarity with AWS services such as S3, Glue, Lambda, Step Functions, and SageMaker or similar cloud technologies.
Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field (or equivalent experience)
Minimum of five (5) years of experience in data engineering, data integration, data warehousing, analytics engineering, or software development involving significant data responsibilities
Strong understanding of data architecture, enterprise systems, and business process automation.
Strong understanding of data governance frameworks.
Knowledge of information security principles and data protection best practices.
Demonstrated ability to lead technical initiatives while managing operational responsibilities.
Understanding of data processing concepts (ETL/ELT).
Understanding of data warehouse architecture and data modeling concepts.

Nice To Haves

Experience with SAP or other enterprise ERP data is preferred.
Familiarity with dbt, Terraform, CI/CD, Git, Java, or similar development and deployment technologies.
Exposure to Generative AI, Large Language Models (LLMs), and enterprise AI platforms is a plus.

Responsibilities

Design, develop, and maintain data pipelines that ingest data from operational systems into the enterprise data warehouse, data lake, and AI/ML environments.
Develop data transformation and loading processes using SQL, Python, and modern data engineering tools.
Integrate data from ERP platforms, SaaS applications, APIs, cloud storage, and other enterprise systems.
Develop and support ingestion processes using cloud services, APIs, file-based integrations, and data integration platforms.
Develop and perform data validation and quality checks to ensure accuracy, completeness, consistency, and reliability.
Identify data anomalies and assist with root-cause analysis and remediation.
Support the preparation, transformation, and delivery of data used by Machine Learning and AI solutions.
Collaborate with AI/ML engineers, developers, and business stakeholders to understand data requirements for model development and production workloads.
Support and maintain cloud data warehouse and data lake environments.
Support platform monitoring, automation, performance optimization, and production stability.
Document pipeline processes, data flows, architecture, integration patterns, dependencies, and support procedures.