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Tier-1 bank, metro location, mid-level data engineer title, and common tech skills drive high competition.
Core data engineering skills transfer across industries, but financial services and ML governance increase specificity.
Multiple mandatory technical skills, regulatory data controls, and certification preferences make shortlisting strict.
Build and maintain scalable data pipelines and feature stores for AI/ML products supporting international markets.
Implement data quality, validation, and monitoring to ensure reliable inputs for models and applications.
Collaborate with AI engineers to productionise data flows and apply data access, privacy, and regulatory controls across borders.
3+ years of applied experience with formal training or certification in software or data engineering concepts.
Proficiency in Python and SQL with hands-on experience in data pipelines and ETL/ELT workflows.
Experience with distributed data processing platforms such as Spark / Databricks and knowledge of cloud-native data services, containerisation, and CI/CD.
BSc degree in Computer Science, Data Engineering, or a related quantitative field.
Experienced in building data infrastructure tailored for ML and agentic AI workflows including feature stores and retrieval-augmented generation pipelines.
Familiar with cross-border data governance, privacy standards, and financial services industry, preferably in wealth or private banking contexts.
Able to engage in technical design discussions, code reviews, and progressively take on technical ownership within a senior associate engineering team.