About

Agung Setiaji is a skilled Data Science professional with over 5 years of experience in data pipeline and automation processes. He has worked in various roles including Data Analyst, Data Scientist, and Data Engineer. As a Data Analyst, he has reported to business users using Excel Sheets for ad-hoc and regular reports and presented analytical findings to business users. He has also built VBA tools for automating tasks used by business users, created store procedures, functional, and view on database platforms for the internal team. As a Data Engineer, Agung has built big data infrastructure for batch and real-time processing, designed OLTP using SQL and NoSQL, designed OLAP using Star Schema Model, and designed ETL/ELT with single and distributed systems as needed. He has also built dashboards for data infrastructure monitoring, built email automation for report and alert, and regularly improved SQL and ETL code for optimization. As a Data Scientist, Agung has built machine learning and predictive modeling such as credit score, churn analysis, customer segmentation, customer profiling, cross-selling/up-selling, survival for predictive maintenance, natural language processing, network analysis, recommendation system, and nearest location search engine. He has also built API data services and machine learning endpoints. Agung is proficient in several tools and technologies including Python, SQL, Bash, Go, Javascript, Scala, R, VBA, Postgres, MySQL, Firestore, MongoDB, BigQuery, SQL Server, Oracle, RedShift, Hive, Hadoop, Airflow, Argo, Kubeflow, MLFlow, Docker Swarm, Kubernetes, Apache Spark, Azure DataBrick, Cloud DataProc, Apache Beam, Cloud Dataflow, Apache Kafka, PubSub, EventHub, Tableau, Metabase, PowerBI, Redash, Shinny, Google Data Studio, Flask, FastApi, Tox, PyTest, Flake8, Autopep8, and Bandit. Agung is currently working as a Data Architect and Engineering at Kompas Gramedia. He has previously worked as a Data Architect and Data Engineer at Larisin, where he developed a scalable data architecture platform using Google Cloud, monitored BigQuery's cost and query optimization as the primary Data Warehouse, and designed ELT with 2 main approaches, Spark Job with Hadoop Cluster for Batch and Kubernetes Job for Micro Batch. He has also assisted in database design and migration, sustained the security and integrity of data using Firebase, Firestore, CloudSQL,