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Strong Tier-1 brand, metro context, and a generic Software Engineer title increase applicant competition.
Big-data engineering skills are broadly transferable, though domain-specific (KYC/finance) knowledge moderately matters.
Multiple mandatory technical platforms and big-data skills create strict filtering for qualified candidates.
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Build and maintain a secure, stable, scalable Global Know Your Customer (KYC) and Risk Assessment Data Platform handling large-scale data processing.
Develop high-quality, reusable software frameworks and production code for data-intensive applications using Python, Java, and big data technologies (PySpark, Databricks).
Collaborate cross-functionally to uphold strong engineering practices, advise on technical solutions, and apply AI-assisted development tools to improve automation and code quality.
Proven experience in system design, application development, testing, and operational stability at enterprise scale.
Strong programming skills in Python and/or Java; experience with big data tools and platforms like PySpark, Databricks, Kafka, Redis, and orchestration frameworks (Airflow, Temporal).
Experience working with cloud-native environments such as AWS, Azure, or GCP and familiarity with relational/NoSQL databases and data lake architectures.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced data engineer comfortable working with large-scale distributed processing and microservices in an agile environment within financial services or similar regulated domains.
Technically proficient with cloud-native data platforms, AI-assisted development tools, and modern big data frameworks (Spark, Apache Iceberg).
Capable of communicating complex technical concepts effectively to senior leaders and peers, promoting best practices in secure, automated software development.