Description
The team builds data pipelines and distributed processing systems that power AI-driven initiatives, supporting Gen AI and ML applications.
Responsibilities
•Design, build, and maintain large-scale data pipelines and distributed processing systems using Spark
•Develop and support data platforms for AI/ML and Generative AI applications
•Architect and implement solutions in AWS cloud environments using cloud-native patterns
•Build and optimize ETL/ELT workflows for analytical and operational use cases
•Implement data modeling strategies aligned with modern data lakehouse architectures
•Ensure data quality, governance, and monitoring across data assets
•Deliver secure, scalable, and highly available data solutions following operational best practices
•Collaborate with cross-functional teams on AI-driven business initiatives
•Write advanced SQL queries and production-grade code in Java, Scala, or Python
Requirements
•5+ Years of Data Engineering experience, including exposure to Gen AI/ML-based applications
•Experience building and maintaining large-scale data pipelines and distributed processing systems using Spark with Java, Scala, or Python
•Familiarity with AI/ML, Generative AI, or data platforms supporting AI-driven initiatives
•Expertise in AWS cloud environments and cloud-native architectures
•Advanced SQL proficiency
•Skills in data modeling and modern data lakehouse architectures
•Understanding of data quality, governance, monitoring, and operational best practices
•Ability to build secure, scalable, and highly available data solutions
•ETL/ELT development experience
•English proficiency at Upper-Intermediate level (B2) or higher
Nice to have
•Hands-on experience with Databricks or Snowflake and the ability to work across modern cloud-based data platforms
•Experience with both platforms
•Background in Financial Services
Conditions
No conditions specified in the vacancy.
