Job Title: Azure Data Analyst (Japanese Bilingual - JLPTN1)
* Must be living and authorized to work in Japan. Interview will be in Japanese Language only.
Technical Architect for Azure Databricks/ ETL processes Data Lake, Azure Synapse, Data Factory.
Within the Lakehouse team, foundational knowledge of SQL, PySpark, and Databricks is essential.
Data SME in the Lakehouse team is expected to work across the following technology stack:
SQL
PySpark
ADF Pipelines
Databricks (Batch and Real-time data)
Fabric Pipelines
Fabric Lakehouse
Fabric Warehouse
Fabric Event house (Real-time Intelligence)
Azure Data Explorer (ADX)
5–6 different accelerators adopted by the Factory team
Responsibilities:
Lead the architecture, design, and implementation of advanced analytics solutions using Azure Databricks/ Fabric. The ideal candidate will have a deep understanding of big data technologies, data engineering, and cloud computing, with a strong focus on Azure Databricks along with Strong SQL.
Work closely with business stakeholders and other IT teams to understand requirements and deliver effective solutions.
Oversee the end-to-end implementation of data solutions, ensuring alignment with business requirements and best practices.
Lead the development of data pipelines and ETL processes using Azure Databricks, PySpark, and other relevant tools.
Integrate Azure Databricks with other Azure services (e.g., Azure Data Lake, Azure Synapse, Azure Data Factory) and on-premise systems.
Provide technical leadership and mentorship to the data engineering team, fostering a culture of continuous learning and improvement.
Ensure proper documentation of architecture, processes, and data flows, while ensuring compliance with security and governance standards.
Ensure best practices are followed in terms of code quality, data security, and scalability.
Stay updated with the latest developments in Databricks and associated technologies to drive innovation.
Must Skills:
Strong with Azure Databricks, including cluster management, notebook development, and Delta Lake.
Proficiency in big data technologies (e.g., Hadoop, Spark) and data processing frameworks (e.g., PySpark).
Deep understanding of Azure services like Azure Data Lake, Azure Synapse, and Azure Data Factory.
Experience with ETL/ELT processes, data warehousing, and building data lakes.
Strong SQL skills and familiarity with NoSQL databases.
Experience with CI/CD pipelines and version control systems like Git.
Knowledge of cloud security best practices.
Power BI for dashboarding and reporting.
Microsoft Fabric for analytics and integration tasks.
Spark Streaming for processing real-time data streams.
Familiarity with Azure Resource Manager (ARM) templates for infrastructure as code (IaC) practices.
Experience:
Demonstrated expertise of 8+ years in developing data ingestion and transformation pipelines using Databricks/Synapse notebooks and Azure Data Factory.
Solid understanding and hands-on experience with Delta tables, Delta Lake, and Data Lake Storage Gen2.
Experience in efficiently using Auto Loader and Delta Live tables for seamless data ingestion and transformation.
Proficiency in building and optimizing query layers using Databricks SQL.
Demonstrated experience integrating Databricks with Azure Synapse, ADLS Gen2, and Power BI for end-to-end analytics solutions.
Prior experience in developing, optimizing, and deploying Power BI reports.
Familiarity with modern CI/CD practices, especially in the context of Databricks and cloud-native solutions.
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