Data Analytics Engineer

• Data Transformation: Converting raw data into metrics, insights, and reports. Building scripts and tools to transform, clean, and prepare data for analysis.
• Data Modeling: Designing and implementing data models for efficient data access and analysis. 
• Data Pipeline Development: Building automated and scalable data pipelines for data ingestion and processing. 
• Data Documentation and Consolidation: Documenting data models, pipelines, and processes to ensure clarity and maintainability and consolidating data sources. 
• Collaboration: Working with data scientists, engineers, analysts, and business stakeholders to address data needs. 
• Data Quality Assurance: Implementing software engineering practices and ensuring clean datasets. Ensuring the accuracy and reliability of data through rigorous testing and quality checks.
• Business Understanding: Understanding business requirements and translating them into data models and insights.
• Performance Monitoring: Monitoring the performance of data pipelines and models to identify and address issues.

Quality and Product Stewardship Responsibilities 

•  Maintains the company standards required of their individual areas of responsibility.
• Notifies Director of IT of any company guideline or procedure deficiencies.

Health, Safety & Environmental Responsibilities

•  Attends and completes all training required to meet regulatory compliance in areas of responsibility.
• Is fully trained on the site Emergency Action Plan (EAP)

Policy 

• Is compliant and follows all company policies (HR, HSSE, Quality)
• Ensures the highest level of confidentiality of data.
• Ensure the highest level of ethical behavior.

• Must have a minimum of a High School diploma or GED and 5 or more years of experience in Information Technology
• SQL: Proficiency in SQL for querying and manipulating data.
• Data Modeling: Knowledge of data modeling techniques and tools.
• Programming Languages: Proficiency in languages like Python, Scala, or R for data manipulation and analysis.
• Data Visualization: Experience with tools like Tableau or Power BI for creating visualizations.
• Data Warehousing: Knowledge of data warehousing concepts and technologies.
• Cloud Computing: Familiarity with cloud platforms like AWS, Azure, or GCP.
• Software Engineering Practices: Applying software engineering techniques like version control and CI/CD.
• Communication and Collaboration: Strong communication and interpersonal skills to collaborate with other data team members and end users.
• Problem-solving: Ability to identify and solve problems related to data quality, accessibility, and performance.