At a Glance
- Tasks: Develop and deliver advanced technology products focused on data and analytics in a dynamic environment.
- Company: Join J.P. Morgan, a global leader in financial services with a commitment to innovation.
- Benefits: Competitive salary, diverse culture, and opportunities for professional growth.
- Other info: Collaborate with cross-functional teams in an agile setting and embrace diversity.
- Why this job: Make an impact by tackling complex cloud data challenges and driving innovation.
- Qualifications: 10+ years of experience in software engineering and AWS Databricks platform administration.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision‑making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.
As a Site Reliability Engineer at JPMorgan Chase within the AIML Data Platforms and Chief Data and Analytics Team, you will develop and deliver advanced technology products focused on data and analytics. Tackle complex cloud data platform challenges, especially around DataLake Tools. In this role you will work in an agile environment, collaborating with cross‑functional teams.
Job Responsibilities:
- Maintains a managed AWS Databricks platform, and provides engineering and operational support for the platform to application teams.
- Performs platform design, set‑up and configuration, workspace administration, resource monitoring, providing engineering support to data engineering teams, Data Science/ML, and Application/integration teams.
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes‑oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.
- Drives continuous improvement in system observability, alerting, and capacity planning.
- Collaborates with engineering and data teams to optimize infrastructure and deployment processes, focusing on automation and operational excellence.
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
- Develops secure high‑quality production code, and reviews and debugs code written by others.
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
- Adds to team culture of diversity, opportunity, and respect.
- Implements Site Reliability Engineering (SRE) best practices to ensure reliability, scalability, and performance of data platforms.
- Develops and maintains incident response procedures, including root cause analysis and postmortem documentation.
Required Qualifications, Capabilities, And Skills:
- Formal training or certification on software engineering concepts and 10+ years applied experience.
- Extensive experience with AWS Databricks platform administration and engineering support is a MUST.
- Strong understanding of SRE principles, including SLIs, SLOs, error budgets, and incident management.
- Experience with monitoring tools, automation frameworks, and CI/CD pipelines.
- Proficient in Python application program development with use of automated unit testing.
- Experience with terraform development and understanding of terraform enterprise.
- Experience in delivering system design, application development, testing, and operational stability.
- Knowledge of Big Data distributed compute frameworks like Spark, Glue, MapReduce etc.
- Excellent troubleshooting, analytical, and communication skills.
- Experience in Data pipelines using Spark.
- Exposure to AWS & Databricks Platform administration.
- Knowledge of containerization (Docker, Kubernetes) and orchestration.
- Familiarity with distributed systems and large-scale data processing.
Preferred Qualifications, Capabilities, And Skills:
- Experience in Data pipelines using Spark.
- Exposure to AWS & Databricks Platform administration.
- Knowledge of containerization (Docker, Kubernetes) and orchestration.
- Familiarity with distributed systems and large-scale data processing.
J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first‑class business in a first‑class way approach to serving clients drives everything we do. We strive to build trusted, long‑term partnerships to help our clients achieve their business objectives. 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.
SRE III- Data & AWS employer: hackajob
J.P. Morgan is an exceptional employer that fosters a dynamic and inclusive work culture, where innovation thrives and employees are empowered to push the boundaries of technology. With a strong commitment to professional development, employees in the SRE III role will have access to cutting-edge resources and collaborative opportunities within a global leader in financial services, all while enjoying the benefits of a diverse workplace that values every individual's contribution.
StudySmarter Expert Advice🤫
We think this is how you could land SRE III- Data & AWS
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like hackajob.
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We think you need these skills to ace SRE III- Data & AWS
Some tips for your application 🫡
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at hackajob, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at hackajob. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at hackajob
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at hackajob!
✨Prepare for Case Studies
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.
