Back
Senior specialized Spark/Python role narrows applicants despite Data Engineer popularity.
Core data engineering skills transfer across industries, though financial-crime domain preference increases specificity.
Explicit 8–12y requirement plus mandatory Spark, Python, and platform experience enforces high shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and maintain scalable data pipelines for processing 80-90 million financial transaction records daily.
Develop high-performance batch and streaming data processing logic using Python and PySpark within a bespoke financial crime analytics platform.
Contribute to data platform architecture and system design, optimizing SQL queries and data models for large-scale processing.
8 to 12 years of professional work experience.
Strong proficiency in Python programming and Apache Spark/PySpark.
Expertise in SQL with ability to optimize queries using window functions and partitioning on PostgreSQL (RDS).
Experience with building end-to-end scalable data pipelines and implementing CI/CD practices, unit testing, and static code analysis.
Engineer with deep coding skills who prefers custom data platform development over purely managed tools.
Experienced with handling very large-scale datasets and optimizing distributed data processing workflows.
Familiarity or experience in financial services or financial crime analytics domain is advantageous but not mandatory.