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Tier-1 brand, popular data-engineer title, metro location, and broad big-data/ML stack increase competition.
Requires deep data engineering and financial services experience, limiting cross-industry transferability.
Mandatory 12+ years and explicit big-data, cloud, and Python requirements make filters highly restrictive.
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Lead application systems analysis and programming activities, ensuring integration across functions and implementing system enhancements for new products and process improvements.
Resolve high-impact problems through in-depth evaluation of complex business and system processes, ensuring application design aligns with architecture standards.
Provide expertise and coaching to mid-level developers, develop coding/testing standards, and ensure compliance with regulations and risk management policies.
12+ years of relevant experience in an Engineering role.
3+ years experience with Big Data technologies such as Apache Spark, Hive, Hadoop, and Storm.
Strong proficiency in Python programming for data manipulation and AI/ML pipeline scripting; knowledge of ELK, Docker, Kubernetes, Azure Cloud, AWS S3, and NoSQL databases (MongoDB, Hbase, Cassandra).
Bachelor’s degree or equivalent experience; Master’s degree preferred.
Senior-level professional experienced in large-scale data engineering for AI/ML applications within financial services or large global environments.
Strong technical expertise in data structures, algorithms, design patterns, and cloud-based Big Data solutions.
Capable of working independently and in matrixed teams, managing multiple projects with tight deadlines while providing technical leadership and process improvements.