Software Engineer III - AWS, Databricks, Python

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 at JPMorgan Chase within the Corporate Technology - FINTECH team, 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 Adds to team culture of diversity, opportunity, inclusion, and respect Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 3+ years applied experience 3+ years of experience using technologies such as Databricks , Pyspark, AWS, is essential and creating ETL Pipeline from scratch is a must. 3+ years of experience working with AWS (Lambda, Step Function, SQS, SNS, API Gateway, secrets manager and storage services) is a must. 3+ years of experience in software engineering and object-oriented programming skills with expertise in Python and Terraform Hands on experience with open-source frameworks/libraries, such as Apache NiFi, Apache Airflow and Autosys. Strong understanding of REST API development using FASTAPI or equivalent frameworks. Advanced at SQL (e.g., joins and aggregations) Preferred qualifications, capabilities, and skills Familiar with development tools such as Jenkins, Jira, Git/Stash, spinnaker Familiarity with unit testing frameworks such as pytest or unittest. Extensive experience in statistical data analysis, with the ability to select appropriate tools and identify data patterns for effective analysis, as well as experience throughout the data lifecycle