AI Data Engineer

Role Overview

This role is centered on building and scaling the data infrastructure that powers advanced analytics and AI-driven applications across a global financial environment. You will design robust, distributed data pipelines that enable real-time and batch processing of large, complex datasets.

What You Will Do

Design and implement scalable distributed data pipelines for both batch and real-time data processing. Build and maintain data ingestion frameworks using streaming technologies (Kafka, Kinesis) and batch processing tools (Spark, Airflow).

Why It Might Be a Fit

Strong proficiency in SQL and Python for large-scale data processing and engineering tasks. Hands-on experience with big data frameworks such as Apache Spark and Hadoop.

Requirements

• Strong proficiency in SQL and Python for large-scale data processing and engineering tasks.
• Hands-on experience with big data frameworks such as Apache Spark and Hadoop.
• Solid experience with streaming systems like Kafka and Kinesis.
• Knowledge of ETL/ELT tools including dbt, AWS Glue, and Airflow.
• Strong understanding of cloud platforms (preferably AWS) including S3, EMR, Lambda, and Glue.
• Experience with data warehousing solutions such as Snowflake, Redshift, or BigQuery.
• Solid understanding of data modeling techniques (star schema, normalization, dimensional modeling).
• Experience building scalable distributed data systems and pipelines.
• Exposure to ML pipelines, feature engineering, and data preparation for AI models.
• Familiarity with data governance, lineage, and monitoring best practices.

Benefits

• Competitive compensation with performance-based incentives.
• Opportunity to work in a global, high-growth financial technology environment.
• Strong career progression opportunities within an international organization.
• Exposure to cutting-edge data engineering and AI-driven systems at scale.
• Collaborative and innovation-focused engineering culture.
• Work environment driven by excellence, ownership, and continuous learning.