## Mission:

A Data Engineer is responsible for designing, building, and maintaining large-scale data systems that enable data-driven decision-making.
They work with various stakeholders to understand data requirements, data architectures, and implement data pipelines to support business intelligence, analytics, and data science initiatives.

## Job Related Experience:

Bachelor’s Degree in MIS/Engineering/Computer Science
Data Warehousing / BI Certification a plus
Advanced in SQL
5+ Experience working with at least two of the top ETL tools: DBT, PWC, ODI, Datastage, …DBT mandatory
3+ years working with cloud environments AWS, Azure, …
3+ years working with Apache Airflow
5+ years working in an IT function
5+ years of BI development, analyst, data modeling, and support experience
5+ years of Relational Database Oracle, SQL Server, ...
or
5+ years of Columnar Database Redshift, ...
3+ years of Spark, Glue, EMR, ...
3+ Experience in scalable python development, (PySpark, Spark SQL)
2+ Experience working with at least two of the top BI tools: Tableau, Qlik, OBIEE, …
Flexible to adapt and quickly (willing to) learning different technologies.

## Others

English C1
Ability to cooperate and work in multicultural environment
Communication and teaching oriented, knowledge transfer ability.
Multi-tasking ability – handling multiple activities in parallel
Organized and structured
Be updated on Scrum methodology
Proactive, flexible, result-driven, with a “can do” attitude, attention to detail, problem-solving

Data Pipeline Development
Build and maintain data pipelines to extract, transform, and load (ETL) data from various sources.

Data Quality & Reliability
Implement data quality checks and validation processes.
Ensure data accuracy, consistency, and reliability across systems.

System Optimization & Performance
Optimize data systems for performance, scalability, and reliability.
Troubleshoot and resolve data-related issues and system problems.

Collaboration & Requirements Gathering
Work closely with data scientists, analysts, architects, and other stakeholders to understand data requirements and deliver appropriate solutions.

Continuous Learning & Innovation
Stay up-to-date with emerging trends and technologies in data engineering.
Explore advancements in predictive and prescriptive modeling to drive continuous improvement.
