Data Pipeline Development: Design, develop, and maintain scalable data pipelines to ingest, process, and store large volumes of data from various sources.
Data Modeling: Create and manage data models to support analytics and reporting needs, ensuring data accuracy and consistency.
ETL Processes: Implement ETL (Extract, Transform, Load) processes to ensure the smooth flow of data from source systems to data warehouses.
Collaboration: Work closely with data analysts and business stakeholders to understand data requirements and deliver data solutions that meet their needs.
Data Quality: Implement data quality checks and monitoring to ensure the reliability and accuracy of data.
Optimization: Optimize data processing and storage for performance and cost efficiency.
Documentation: Maintain comprehensive documentation of data pipelines, models, and processes.
AdHoc Querying & Data Integrations: Assist Business Systems team with adhoc querying and reporting from ERP/CRM systems. Help with data integrations between these systems and our custom built applications.
Innovation: Stay up-to-date with industry trends and best practices in data engineering and analytics, and apply them to improve existing processes.
Understand and display WLT’s values.
Other duties as assigned
Requirements & Skills:
Education: Bachelor’s degree in Computer Science, Data Science, Information Technology, or a related field. A master’s degree is a plus.
Experience: 3+ years of experience in data engineering, analytics, or a related field.
Technical Skills: Proficiency in Microsoft SQL, dBT, Python, and/or R. Experience with data pipeline tools (e.g., Apache Airflow, Luigi) and data warehousing solutions (Azure SQL, Azure Fabric, Azure Synapse).
Tools: Familiarity Microsoft Power BI.
Cloud Platforms: Experience with cloud platforms like Azure Fabirc.
Problem-Solving: Strong analytical and problem-solving skills with a keen attention to detail.
Communication: Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
Knowledge of machine learning and predictive analytics.
Experience with version control systems (e.g., Git).
Understanding of data governance and security best practices.
