Data Analytics Engineer I

G&W Electric Co
Bolingbrook, IL, United States
about 1 month ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Starter
Experience required
0 years minimum
Working hours
Regular working hours

Tech stack

Artificial Intelligence Business Analytics Applications Data Analysis Computing Platforms Information Systems Data Architecture Data Validation Information Engineering Data Governance Data Integration Extract Transform Load (ETL) Data Transformation
+16 more
Database Development Document-Oriented Databases Python (Programming Language) Machine Learning Meta-Data Management Cloud Services Standard Sql SQL Databases Cloud Platform System Data Ingestion Information Technology Data Lineage Data Analytics Performance Monitor Operational Systems Data Pipelines

Job description

The Data Analytics Engineer I supports the design, development, maintenance, and optimization of the enterprise analytics data platform. This role assists with data pipelines, data models, integration processes, data quality controls, and analytical capabilities that support reporting, AI initiatives, automation, and enterprise decision making., The Engineer I works with Data Analytics Engineers, Developers, Analysts, and business stakeholders to ensure enterprise data is reliable, accessible, and usable for analytics solutions. This role is intended for an early-career data engineering professional with strong technical aptitude who can contribute to data integration, data modeling, SQL development, documentation, testing, and platform support while continuing to build deeper expertise in enterprise data architecture and advanced analytics., * Design, develop, test, and maintain data pipelines and integration processes under appropriate guidance.

  • Build and support data models used for enterprise analytics, dashboards, reporting, and business semantic layers.
  • Support data ingestion from ERP, CRM, manufacturing, quality, and other operational systems.
  • Develop and maintain data quality monitoring, validation, and reconciliation processes.
  • Support Incorta platform architecture, data modeling, performance monitoring, and optimization activities.
  • Develop SQL scripts, data transformations, and technical solutions to support analytics needs.
  • Support Python-based automation, analytics, and AI-related solutions.
  • Assist with machine learning, predictive analytics, and intelligent automation initiatives.
  • Implement data governance standards, metadata management practices, and technical documentation requirements.
  • Document data architecture, data lineage, integration processes, technical standards, and support procedures.
  • Collaborate with Developers and Analysts to ensure data availability, consistency, accuracy, and usability.
  • Troubleshoot data issues, pipeline failures, performance concerns, and reporting discrepancies.
  • Build knowledge of enterprise source systems, data architecture, cloud data platforms, AI technologies, and analytics engineering practices.

Requirements

  • Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Analytics, Data Science, or related field.
  • 0-3 years of experience in data engineering, database development, ETL/ELT development, data integration, analytics engineering, reporting data models, or related technical work.
  • Strong SQL and database development skills.
  • Experience with ETL/ELT processes, data modeling, and data transformation.
  • Strong analytical, troubleshooting, and problem-solving capabilities.
  • Understanding of data quality, data validation, and reconciliation practices.
  • Ability to work collaboratively with technical teams and business stakeholders.
  • Strong attention to detail and commitment to reliable, accurate data.
  • Ability to learn enterprise platforms, source systems, data architecture, and emerging analytics technologies.

Preferred Qualifications

  • Experience with Incorta.
  • Experience with Python development.
  • Familiarity with machine learning and AI technologies.
  • Exposure to cloud-based data platforms.
  • Manufacturing and ERP data experience.
  • Experience with metadata management, data lineage, or data governance practices.
  • Experience supporting data pipelines for reporting, dashboards, or analytical applications.

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