Data Engineer  

Job Description:

• Analyze, understand, and maintain existing Python-based scripts used for data extraction, transformation, and automation processes across various systems.
• Design and develop robust data transformation processes to convert unstructured and semi-structured data (such as JSON, XML, log files, and API responses) into structured, tabular datasets for reporting and analytics purposes.
• Build, manage, and optimize ETL/ELT workflows, ensuring data consistency, integrity, and reliability across multiple data sources and destinations.
• Develop scalable data models and transformation logic to support downstream analytics, reporting, and operational use cases.
• Monitor data pipelines proactively, identify bottlenecks or failures, and troubleshoot issues to minimize downtime and ensure smooth data operations.
• Collaborate with data analysts, business stakeholders, and engineering teams to gather requirements and implement efficient data solutions aligned with business needs.
• Optimize SQL queries and database performance for large-scale data processing, including managing joins, aggregations, indexing strategies, and granularity handling.
• Implement best practices in data quality validation, error handling, logging, and documentation to maintain high standards in data engineering workflows.
• Support continuous improvement initiatives by identifying opportunities to enhance existing pipelines, tools, and data infrastructure.

Job Details:

• Willing to working on-site
• PKWT Contract (extendable)
• BPJS (TK, KK), THR (1x), Bonus (1x), Yearly Income (14x)
• Medical Insurance (Individual Gold - AdMedika)
• Paid Leave (12 days/year)
• Laptop for working
• Overtime Pay (if any)

Requirements:

• 1–5 years of experience in Data Engineering, ETL development, or related data-focused roles.
• Strong proficiency in Python, with proven ability to read, analyze, and enhance existing codebases for data processing and automation.
• Hands-on experience in transforming unstructured or semi-structured data into structured formats suitable for analytics and reporting.
• Solid understanding of ETL/ELT concepts, data integration techniques, and workflow orchestration.
• Strong SQL expertise, including complex joins, query optimization, data granularity management, and performance tuning.
• Experience with Azure Data Factory (ADF) for building and orchestrating data pipelines is highly preferred.
• Familiarity with dbt (data build tool) for data transformation and modeling is an advantage.
• Strong understanding of data quality principles, validation processes, and troubleshooting techniques in data pipelines.
• Excellent analytical thinking and problem-solving skills, with the ability to resolve technical issues efficiently.
• Good communication and collaboration skills, with the ability to work effectively in cross-functional teams.