Lead Software Engineer - Data, AWS

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Commercial and Investment Bank - Securities Services Technology team, you play a crucial role in an agile team dedicated to enhancing, building, and delivering reliable, market-leading technology products that are secure, stable, and scalable. As a key technical contributor, you will be responsible for implementing essential technology solutions across diverse technical domains, supporting various business functions to achieve the firm's business goals.

Job responsibilities

• Leads and mentors a team of data engineers and manage delivery using agile methodologies
• Designs, develops and maintains scalable data pipelines, transform data to a LDM supporting data analytics and business intelligence initiatives
• Drives decisions that influence the product design, application functionality, and technical operations and processes
• Involves designing, developing, and deploying solutions on AWS to solve complex business problems.
• Collaborates with cross-functional teams to understand business requirements and formulate technical solutions.
• Manages business stakeholders with product roadmap and delivery

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Experience in data platforms like Databricks, Redshift etc.
• Familiarity with AWS cloud platforms services like ECS, Lambda, Glue, Step Functions, SQS, RDS Aurora etc.
• Working experience with Data Lake Architecture
• Working experience with IaC like Terraform, Ansible etc.
• Proficiency in programming languages such as Enterprise Java or Python,
• Experience in technologies like Spark and Kafka
• Develop and deploy scalable Generative AI solutions.
• Conduct research and experiments to explore new AI/ML techniques and methodologies.
• Good Understanding of data privacy, security best practices, and compliance requirements to ensure safe and ethical use of LLMs.