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SQL Server, SSIS, dbt, Airflow, BigQuery, Python — the tools I use to build data pipelines.
Design and optimize complex queries, stored procedures, and data transformation logic for enterprise banking environments.
Write advanced T-SQL including window functions, CTEs, and dynamic SQL for large-scale data pipelines.
Build and maintain ETL pipelines using SQL Server Integration Services for production banking data workflows.
Design and optimize production-grade data pipelines handling large-scale transformation and loading for enterprise clients.
Develop data processing scripts, NLP models, and machine learning solutions using Python and its ecosystem.
Work with relational databases for data storage, querying, and analytics in various project environments.
Perform data validation, reporting, and analysis using Excel in enterprise client environments.
Version control and collaboration using Git for code management in data engineering projects.
Build modular, version-controlled data transformation workflows using dbt for modern ELT pipelines.
Orchestrate and schedule data pipelines with Apache Airflow for reliable workflow automation.
Leverage Google Cloud Platform and BigQuery for scalable cloud data warehousing and analytics.
Build and evaluate machine learning models including classification, random forest, and neural networks.
Develop NLP solutions including Indonesian text correction and sentiment analysis using transformer-based models.
Build interactive dashboards, reports, and BI solutions using Looker for data exploration and visualization.