Software Data Engineer Sr. Associate - Python/BigData
- Compensation
- Not disclosed
- Location
- Buenos Aires, Argentina
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Proficiency in advanced Python programming, with extensive experience in utilizing libraries such as Pandas and NumPy.
- Experience in code and infrastructure for Big Data technologies (e.g. Spark, Kafka, Databricks etc.) and implementing complex ETL transformations
- Experience with AWS services including EC2, EMR, ASG, Lambda, EKS, RDS and others
- Experience developing APIs leveraging different back-end data stores (RDS, Graph, Dynamo, etc.)
- Experience in writing efficient SQL queries
- Strong understanding of linear algebra, statistics, and algorithms.
- Strong Experience with UNIX shell scripting to automate file preparation and database loads
- Experience in data quality testing; adept at writing test cases and scripts, presenting and resolving data issues
- Familiarity with relational database environment (Oracle, SQL Server, etc.) leveraging databases, tables/views, stored procedures, agent jobs, etc.
- Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy
- Strong development discipline and adherence to best practices and standards.
Nice to have
- Experience in Data Science, Machine Learning and AI is a plus
- Financial Services and Commercial banking experience is a plus
- Familiarity with NoSQL platforms (MongoDB, AWS Open Search) is a plus
Full description
As a Software Engineer III at JPMorgan Chase within the Commercial & Investment Bank, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
The JPMorgan Chase Commercial & Investment Bank is undertaking a strategic, initiative called Client 360 aimed at developing a big data platform and Firmwide solution for Entity Resolution and Relationships. We are seeking a Big Data Software Engineer with skills and experience implementing large-scale, cloud platform processing internal and 3rd party data. This individual will work on groundbreaking work to implement new solutions for Client 360 - Entity Resolution and Relationships and enhance the existing platform.
Job responsibilities
- Acquire and manage data from primary and secondary data sources
- Identify, analyze, and interpret trends or patterns in complex data sets
- Transform existing ETL logic on AWS and Databricks
- Innovate new ways of managing, transforming and validating data
- Implement new or enhance services and scripts (in both object-oriented and functional programming)
- Establish and enforce guidelines to ensure consistency, quality and completeness of data assets
- Apply quality assurance best practices to all work products
- Analyze, design and implement business-related solutions and core architectural changes using Agile programming methodologies with a development team
- Comfortable learning cutting edge technology stacks and applications to greenfield projects
Qualifications
- Proficiency in advanced Python programming, with extensive experience in utilizing libraries such as Pandas and NumPy.
- Experience in code and infrastructure for Big Data technologies (e.g. Spark, Kafka, Databricks etc.) and implementing complex ETL transformations
- Experience with AWS services including EC2, EMR, ASG, Lambda, EKS, RDS and others
- Experience developing APIs leveraging different back-end data stores (RDS, Graph, Dynamo, etc.)
- Experience in writing efficient SQL queries
- Strong understanding of linear algebra, statistics, and algorithms.
- Strong Experience with UNIX shell scripting to automate file preparation and database loads
- Experience in data quality testing; adept at writing test cases and scripts, presenting and resolving data issues