JPMorgan Chase within Wealth Management is looking to design and deliver reliable data collection, storage, access, and analytics solutions that are secure, stable, and scalable.
Requirements
- Advanced proficiency in NoSQL databases and SQL (e.g., joins and aggregations)
- Proficiency in programming languages such as Java and Python for data processing tasks
- Proficient in Object-Oriented Programming (OOP) concepts, with a strong ability to design and implement robust, reusable, and maintainable code structures across various programming languages
- Extensive experience with cloud platforms, particularly Amazon Web Services (AWS), including EMR, Glue, Lambda, and ECS, to design, deploy, and manage scalable and efficient cloud-based solutions.
- Hands-on experience with frameworks like Apache Spark, leveraging its capabilities for large-scale data processing and analytics to drive efficient and insightful data solutions.
- Proven experience in utilizing Cucumber and Gherkin for behavior-driven development (BDD)
- Proficiency in Unix scripting, data structures, data serialization formats such as JSON, AVRO, or similar, and big-data storage formats such as Parquet
Responsibilities
- Designs, develops, and maintains robust data pipelines to automate the extraction, transformation, and loading (ETL) of data from various sources into data warehouses or data lakes
- Implements scalable and efficient data architectures that support data processing and analytics
- Integrates data from multiple sources, ensuring consistency, accuracy, and reliability
- Monitors and troubleshoots data pipeline performance issues and implements solutions
- Documents data pipeline processes, architectures, and workflows for future reference and training
- Updates logical or physical data models based on new use cases
- Reviews and makes customizations in one or two tools to generate a product at the business or customer's request
Other
- Supports the review of controls to ensure sufficient protection of enterprise data
- Contributes to the team's culture by promoting collaboration and innovation
- Collaborates with data scientists and analysts to understand data requirements and provide solutions
- Formal training or certification on data engineering disciplines and 3+ years applied experience
- Flexibility and eagerness to learn new technologies and skills
