Data Engineer

Overview

5–7 years of experience in data engineering and data governance within industrial domains (CPG & Manufacturing). Strong knowledge of Azure Data Services: Azure Data Factory, Azure Databricks, Azure Data Lake Gen2 Expertise in ETL processes, data integration tools, and pipeline development. Proficiency in Python, SQL, and Apache Spark. Understanding of Industry 4.0, ISA-95 standards, and maintenance strategies. Familiarity with AI/ML concepts and ability to work with advanced analytics teams. Experience with DevOps (CI/CD) for data pipeline automation.

Design and develop data pipelines using Azure Data Factory, Azure Databricks, and Azure Data Lake Gen2. Implement ETL/ELT processes, data integration techniques, and data governance standards. Work with Big Data technologies such as Apache Spark and PySpark for large-scale data processing. Gather, engineer, and prepare data for analytics and business insights. Integrate systems and data across diverse environments, ensuring seamless connectivity. Build solutions to extract and consolidate data from multiple sources (sales, inventory, product, customer databases). Collaborate with ML Engineers, Data Scientists, and business stakeholders to enable automation and advanced analytics. Contribute to coding standards and best practices for data engineering workloads. Support modernization initiatives aligned with Industry 4.0 and ISA-95 standards.

Good to have: Exposure to data modeling, data warehousing, and cloud architecture. Knowledge of business intelligence tools and statistical programming. Ability to research and implement new tools for automation and optimization.