Back
Tier-1 brand, mid-level data engineer in metro with broad ML-data stack, high applicant density.
Data engineering skills transferable, but financial-regulatory and ML-specific experience raise domain specificity.
Explicit 3+ years, mandatory data engineering stack, and regulated financial-services controls tighten filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Build and maintain scalable data pipelines and feature stores to support AI/ML products from source systems to production.
Implement data quality, validation, and monitoring to ensure trustworthiness of data inputs for models and applications.
Collaborate with AI engineers to productionise data flows for agentic AI and ML use cases while ensuring compliance with data privacy and residency controls.
Minimum 3 years applied experience in data engineering or related field with formal training or certification.
Proficiency in Python and SQL with hands-on experience building and maintaining data pipelines and ETL/ELT workflows.
Familiarity with distributed data processing platforms such as Spark/Databricks and cloud-native data services, containerisation, and CI/CD.
BSc in Computer Science, Data Engineering, or related quantitative field.
Experienced in developing data infrastructures specifically for AI/ML workloads including feature stores and RAG pipelines.
Comfortable working within regulated environments applying data governance, privacy, and cross-border data residency standards.
Capable of contributing technically through design discussions, code reviews, and using AI coding tools such as GitHub Copilot or Claude Code.