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Tier-1 brand, metro location, popular Data Engineer title, and mid-level experience amplify competition.
Core data engineering skills highly transferable, though finance domain knowledge moderately matters.
Mandatory 5+ years and many required technologies enforce strict technical filters.
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Lead development of secure, scalable production code for data-intensive applications in Global KYC and Risk Assessment Data Platforms.
Define and drive adoption of architecture, engineering standards, and advanced technical practices across multiple teams.
Mentor engineers and advise cross-functional teams on technology matters, improving automation and leveraging SDLC tools including AI-assisted development.
5+ years of applied experience with formal software engineering training or certification.
Expertise in one or more programming languages, specifically Python and/or Java.
Hands-on experience with large-scale data processing, microservices, API design, Kafka, Redis, observability tools, orchestration frameworks, and cloud platforms (AWS/Azure/GCP).
Advanced knowledge in software application development, relational and NoSQL databases, data lake architectures, and data governance.
Experienced in enterprise-scale system design and operational stability within data engineering and risk management domains.
Strong background in cloud-native technologies and modern data platforms such as Databricks, Snowflake, and big data processing tools like Spark/PySpark.
Comfortable engaging with senior leadership and driving cross-team technical strategies and innovations.