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Tier-1 brand, mid-level experience requirement, and metro location increase applicant competition despite niche data skills.
Technical data engineering skills are transferable, but financial KYC/risk domain preference raises domain specificity.
Mandatory 5+ years, lead seniority, and specific data engineering stack make shortlisting fairly strict.
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Lead development of secure, scalable data-intensive applications for Global KYC and Risk Assessment Data Platform.
Define architecture, engineering standards, and best practices across multiple teams.
Mentor engineers and advise cross-functional teams on advanced technical methods and automation improvements.
5+ years of applied software engineering experience with formal training or certification.
Expertise in Python and/or Java programming languages.
Experience with large-scale data processing, microservices, API design, and relevant tooling like Kafka, Redis, Airflow, observability tools.
Practical cloud-native experience on AWS, Azure, or GCP.
Strong background in data engineering, cloud-native application development, and enterprise-scale system design.
Experienced in leading engineering efforts and defining practices across multiple teams with emphasis on risk or financial data platforms.
Familiarity with advanced data platforms (Databricks, Snowflake), Spark/PySpark, open-source table formats like Apache Iceberg, and AI/ML tooling.