Lead Data Engineer- Oracle / Cloud Services

Key Skills

Agile methodologies, Algorithms, Amazon RedShift, Apache Airflow, Apache Cassandra, Apache Kafka, Azure, Caffe, Cloud computing, Computer Vision (CV), Continuous Integration & Continuous Delivery - CI/CD, Data Mining, Data science techniques, Database, Design, Docker, DynamoDB, Elasticsearch, ETL frameworks, Geometry, Google Cloud Platform (GCP), HDFS, Java Programming, Keras software library, Kubernetes-K8s, Leadership, Linear Algebra, Machine learning techniques, MxNet, Natural Language Processing (NLP), NoSQL, Oracle, Python Programming, PyTorch, Reinforcement Learning, Scikit-learn, Statistics, TensorFlow, Time series

Job Description

Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.

As a Lead Data Engineer- Oracle / Cloud Services at JPMorgan Chase within Asset Wealth Management, GPB Technology, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Generates data models for their team using firmwide tooling, linear algebra, statistics, and geometrical algorithms
• Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way
• Implements database back-up, recovery, and archiving strategy 
• Evaluates and reports on access control processes to determine effectiveness of data asset security​ with minimal supervision
• Data domain expert and drive to know everything about the data on the platform
• Creates Functional and Technical Specifications, Epics and User Stories, Process Flows, Data Analysis, Mapping Documents, Implementation Plan, Agile artifacts; Creates complex SQL queries to support data analysis; Develops secure high-quality production code, and reviews and debugs code written by others; Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture; Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies

Required qualifications, capabilities, and skills

• Working experience with both relational and NoSQL databases​
• Experience and proficiency across the data lifecycle
• Experience with database back-up, recovery, and archiving strategy
• Proficient knowledge of linear algebra, statistics, and geometrical algorithms
• Formal training or certification in software engineering concepts and 5+ years of Database experience, preferably in Oracle
• Familiar with Data Science concepts and applying them to analyze large volumes of data
• Comfortable with streaming and big data concepts; Oracle, Java, Python, Spark, Flink, Kafka, HDFS, AirFlow, Elastic Search, Cassandra and AWS Cloud Services (MSK, Aurora, DynamoDB, Redshift, etc.); Advanced understanding of agile methodologies such as CI/CD, Applicant Resiliency, and Security; Experience in ETL pipelines, both batch and real-time data processing.

Preferred qualifications, capabilities, and skills 

• Knowledge in Machine Learning, Data Mining, Information Retrieval, Statistics is a big plus; Exposure in at least one of the following areas: Natural Language Processing, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis; Exposure to machine learning frameworks: Tensorflow, Caffe/Caffe2, Pytorch, Keras, MXNet, Scikit-Learn
• Cloud computing: Google Cloud, Amazon Web Service, Azure, Docker, Kubernetes
• Experience in distributed system design and development