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Strong Tier-1 brand, metro location, and mid-level data role increase applicant competition despite technical specialization.
Core data engineering skills transfer across industries, though regulated banking domain adds moderate specificity.
Extensive mandatory big-data, Databricks, cloud, and enterprise tooling requirements create strict technical shortlisting filters.
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Engineer and deliver a secure, stable, and scalable Global Know Your Customer (KYC) and Risk Assessment Data Platform in an enterprise environment.
Develop reusable, high-quality production code and software frameworks for data-intensive applications, focusing on data engineering and big data technologies.
Collaborate with cross-functional teams and advise on technology within your domain, leveraging automation and enterprise-authorized AI development tools to enhance productivity and code quality.
Proven experience in system design, application development, testing, and maintaining operational stability at enterprise scale.
Expertise in Python and/or Java programming languages; strong knowledge of data engineering, cloud-native environments (AWS, Azure, or GCP), and large-scale data processing technologies (Spark/PySpark).
Experience with microservices, API design, Kafka, Redis, Memcached, observability tools (Dynatrace, Splunk, Grafana), orchestration frameworks (Airflow, Temporal), and both relational and NoSQL databases.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced software engineer with deep technical skills in big data engineering and cloud-native data ecosystems, especially with platforms like Databricks and Snowflake.
Comfortable working with modern data management standards including open table formats and metadata/catalog services (e.g., Apache Iceberg).
Skilled in the use of enterprise-authorized AI-assisted development tools and capable of applying responsible AI engineering and validation practices within a complex, regulated environment.