At a Glance
- Tasks: Design and maintain critical data distribution systems using AWS and Snowflake.
- Company: Join JPMorgan Chase, a global leader in financial services with a focus on innovation.
- Benefits: Competitive salary, diverse workplace, and opportunities for professional growth.
- Other info: Embrace diversity and inclusion while advancing your career in a dynamic team.
- Why this job: Make a real impact by working on cutting-edge technology in a collaborative environment.
- Qualifications: Experience in cloud technologies, programming, and data engineering required.
The predicted salary is between 60000 - 80000 £ per year.
As a Lead Engineer at JPMorgan Chase within our Market Data Services team you will play a pivotal role in designing, delivering and maintaining critical data distribution systems within the firm. These systems encompass both on-premises and cloud based infrastructure to deliver third party data to multiple lines of business. You will be working within an Agile project environment collaborating closely with cross-functional teams to deliver high-quality solutions to our clients.
Applies deep technical expertise and problem-solving methodologies focused on analyzing complex data and systems, anticipating issues, considering upstream and downstream implications, and advising on mitigation actions. Uses enterprise-authorized AI capabilities within the work environment to accelerate analysis of complex infrastructure signals and documentation of mitigation options, validating outputs and handling operational data according to sensitivity and security requirements.
Designing and maintaining critical data delivery systems - encompassing both realtime data, historical data and AI/ML use-cases. Collaboration with product owners and stakeholders to ensure data solutions align with business and regulatory requirements. Ability to present to senior management. A respect for strong process and control management disciplines.
Leads reuse-first adoption of AI-assisted practices across delivery and automation routines to reduce recurring issues, ensuring changes are validated, traceable and auditable, and aligned to resiliency and security expectations. Formal training or certification on infrastructure engineering concepts and advanced applied experience in Cloud technologies including Kubernetes, Terraform and AWS. Data Lake technologies (e.g. Snowflake, Databricks, AWS Glue/Athena, Lake Formation). Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations. Network architecture and protocols. Programming languages (e.g. Java, Python, C++). Market Data / messaging products (e.g. Market Data vendors and their key products (e.g. Database technologies and SQL scripting). Monitoring tools (ITRS Geneos, Dynatrace, Datadog).
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Lead Infrastructure Engineer - Market Data - AWS / Snowflake (London)
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We think you need these skills to ace Senior Lead Infrastructure Engineer - Market Data - AWS / Snowflake (London)
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Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
