Senior Data Engineer

Risk Management team at ZaloPay gather highly motivated team players who dedicate in risk assessment, analyzing fraud & abuse patterns and then creating and adapting advanced prevention mechanisms while focusing on the user and customer experience.
We are looking for a savvy Data Engineer to join our Risk Tech & Platform team. The hire will be responsible for expanding and optimizing our data collection and flow, data pipeline architecture, as well as maintaining the quality of them. The ideal candidate is an experienced data platform builder and data wrangler who enjoys optimizing data systems and building them from the ground up. The Data Engineer will support our software developers, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products.
As a Data Engineer in our team, you will have the opportunity to:

• Create and maintain optimal data pipeline architecture;
• Identify, design, and implement internal process enhancement: automating manual processes, optimizing data delivery & infrastructure for greater scalability, etc.
• Assemble large, complex datasets that meet functional or non-functional business requirements;
• Develop the solution required for optimal ETL of data from a wide variety of data sources;
• Work with stakeholders including the Risk Policy & Operation, Risk Tech & Platform, Risk Data Science team members to assist with data-related technical issues and support their data needs;
• Work closely with Risk Data Science team to build data analytics products by utilizing the data pipeline and implementing necessary services to provide actionable insights into operational efficiency and other key risk performance metrics, for example, risk monitoring & investigation tool, risk simulation tool.

We are looking for a candidate with 2+ years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools:

• • Big data tools: Hadoop, Spark, Kafka, etc.
  • Relational SQL and NoSQL databases, including MySQL, MongoDB & ArangoDB;
  • Data pipeline and workflow management tools: Luigi, Airflow, etc.
  • Stream-processing systems: Spark-Streaming, etc.
  • Object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
• Experience in building and optimizing 'big data' data pipelines, architectures and data sets;
• Experience in performing root cause analysis on internal, external data and processes to answer specific business questions and identify opportunities for improvement;
• Experience in manipulating, processing and extracting value from large disconnected datasets;
• Experience in supporting and working with cross-functional teams in a dynamic & fast-paced environment.