Job Description

About the Role 

We are looking for a Data Engineer II to join our Data & Analytics organization within Product and Technology. In this role, you will design, build, and maintain the data pipelines and models that transform raw data into accessible, reliable, and actionable information for the business. You will work closely with Analytics, Product, and Engineering teams to ensure our data infrastructure is scalable, high-quality, and ready to support data-driven decision-making, including emerging AI-enabled capabilities. 

What You Will Do 

• Build and improve the pipelines and models behind Zafin Data Fabric.  
• Work across ingestion, data transformations, data models and tests, and orchestration. Investigate data-quality or reliability issues end to end, document reusable solutions, and collaborate directly with colleagues who understand the source system or banking use case. 
• Investigate data-quality and reliability issues end to end, document reusable solutions, and collaborate directly with colleagues who understand the source system or banking use case. 
• Design, build, and maintain scalable ETL/ELT pipelines that reliably move data from source systems into the data platform, with reusable transformations, tests, and orchestration. 
• Monitor and improve data pipeline performance, reliability, and cost-efficiency. 
• Partner with Analytics and Product teams to understand data requirements and translate them into well-structured data assets. 
• Implement data quality checks and proactively identify and resolve data issues. 
• Support the adoption of modern data engineering practices, including orchestration, testing, and CI/CD for data pipelines. 
• Document data flows, models, and pipeline architecture for maintainability. 
• Contribute to the evaluation and adoption of new data tools and technologies, including AI-assisted data engineering capabilities. 

What You Need to Succeed 

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent experience. 
• 3–5+ years of experience in data engineering, analytics engineering, or a similar role, preferably in banking, fintech, or enterprise SaaS environments. 
• Experience with data pipeline orchestration tools (e.g., Airflow, Prefect, Dagster or similar). 
• Experience with cloud data platforms (AWS, Azure, or GCP) and their data services. 
• Familiarity with big data technologies such as Spark, Kafka, or similar streaming/processing frameworks. 
• Strong data modeling and data warehousing skills. 
• Strong analytical and problem-solving skills, with attention to data accuracy and consistency and the ability to communicate clearly and confidently in English, in writing and conversation. 
• Interest in learning unfamiliar tools and business concepts and staying current with changes in data technology and engineering practices. 
• Ownership beyond assigned sprint tasks, including investigating problems, seeking out the people and context needed, making progress independently where possible, and raising blockers early with a clear account of what has been tried. 
• Ability to work well across Product, Engineering, and customer-facing teams, with an interest in understanding the banking problem behind each technical requirement. 

Nice to Have 

• Experience with banking platforms, financial products, or enterprise SaaS data models. 
• Familiarity with AI-assisted data engineering or ML pipeline tooling. 
• Cloud data certification (e.g., Google Cloud Professional Data Engineer, AWS Certified Data Analytics). 
• Experience working in an Agile delivery model alongside Engineering and QA. 

Success in This Role Looks Like 

• Data pipelines run reliably, at scale, with strong data quality and minimal manual intervention. 
• Business stakeholders can trust and act on the data models and reports built on top of this role's work. 
• Data issues are identified and resolved proactively, before they affect downstream reporting or decisions. 
• This role is seen as a reliable, go-to resource for data infrastructure within the team. 

Required Skills

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