Data Engineering

As a Data Engineer at Hitachi Energy Indian Operations Center (INOPC), your role will involve displaying technical expertise in AI data analytics to a team with diverse technical competencies. Your responsibilities will include: - Building and maintaining accurate and scalable data pipeline and infrastructure such as SQL Warehouse, Data Lakes using Cloud platforms like MS Azure and Databricks. - Working proactively with business stakeholders to understand data lineage, definitions, and methods of data extraction. - Writing production grade SQL and PySpark code to create the data architecture. - Consolidating SQL databases from multiple sources, data cleaning, and manipulation for analytics and machine learning. - Utilizing data visualization tools such as Power BI to create professional quality dashboards and reports. - Writing high-quality documentation for data processing in different projects to ensure reproducibility. Your qualifications should include: - BE / BS in Computer Science, Data Science, or a related discipline with at least 5 years of relevant working experience. - 5 years of data engineering experience with an understanding of lakehouse architecture, data integration framework, ETL/ELT pipeline, and star schema data modeling. - Proficiency in Python/PySpark, SQL, and Microsoft Power BI. - Experience in utilizing Azure-based environments like Databricks, ADLS, and Azure Machine Learning for data engineering purposes. - Basic understanding of Machine Learning algorithms. - Ability to quickly grasp concepts from fields beyond your core competency and solve problems independently in a matrixed corporate environment. - Proficiency in both spoken and written English language. Join Hitachi Energy's INOPC team in Chennai, Tamil Nadu, India, and be a part of a global value chain empowering Hitachi Energy customers worldwide with optimized value through data engineering expertise. Apply now to contribute to Hitachi Energy's Global Foot Print strategy. (Note: The additional company details were not provided in the job description.) As a Data Engineer at Hitachi Energy Indian Operations Center (INOPC), your role will involve displaying technical expertise in AI data analytics to a team with diverse technical competencies. Your responsibilities will include: - Building and maintaining accurate and scalable data pipeline and infrastructure such as SQL Warehouse, Data Lakes using Cloud platforms like MS Azure and Databricks. - Working proactively with business stakeholders to understand data lineage, definitions, and methods of data extraction. - Writing production grade SQL and PySpark code to create the data architecture. - Consolidating SQL databases from multiple sources, data cleaning, and manipulation for analytics and machine learning. - Utilizing data visualization tools such as Power BI to create professional quality dashboards and reports. - Writing high-quality documentation for data processing in different projects to ensure reproducibility. Your qualifications should include: - BE / BS in Computer Science, Data Science, or a related discipline with at least 5 years of relevant working experience. - 5 years of data engineering experience with an understanding of lakehouse architecture, data integration framework, ETL/ELT pipeline, and star schema data modeling. - Proficiency in Python/PySpark, SQL, and Microsoft Power BI. - Experience in utilizing Azure-based environments like Databricks, ADLS, and Azure Machine Learning for data engineering purposes. - Basic understanding of Machine Learning algorithms. - Ability to quickly grasp concepts from fields beyond your core competency and solve problems independently in a matrixed corporate environment. - Proficiency in both spoken and written English language. Join Hitachi Energy's INOPC team in Chennai, Tamil Nadu, India, and be a part of a global value chain empowering Hitachi Energy customers worldwide with optimized value through data engineering expertise. Apply now to contribute to Hitachi Energy's Global Foot Print strategy. (Note: The additional company details were not provided in the job description.)