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- Tasks: Design and build data pipelines to drive business insights and AI solutions.
- Company: Join JPMorgan Chase's innovative Security Services team.
- Benefits: Competitive salary, career growth, and a collaborative work environment.
- Why this job: Make an impact by enabling data-driven decisions in a leading financial institution.
- Qualifications: Experience with Databricks, Python, and data pipeline development.
- Other info: Dynamic role with opportunities for professional development and teamwork.
The predicted salary is between 36000 - 60000 £ per year.
Be part of a team that creates the strategic data assets driving business insight, operational excellence, and the next generation of AI solutions. Your work will directly enable the business to answer key questions, track progress on objectives, and unlock new opportunities through data.
As a Data Engineer in the Security Services Data Modelling and Engineering team, within AI Transformation, you will play a pivotal role in building the data foundation that powers business insights, OKR tracking, and AI enablement across JPMorganChase’s Security Services businesses. You will design and develop scalable data pipelines and reusable datasets on Databricks, collaborating with Data Architects and Business Analysts to deliver high-quality, compliant, and business-driven solutions.
Job responsibilities:- Design, build, and optimize data pipelines and transformation workflows on Databricks, leveraging Python and Spark.
- Collaborate with Data Architects and Business Analysts to develop robust data models and clearly document data flows and ETL logic.
- Implement and execute data quality checks and validation modules using Python.
- Maintain transparency and accountability by tracking work and progress in Jira.
- Ensure datasets and pipelines are accurately registered in relevant catalogues and consoles, meeting governance and privacy standards.
- Proven experience developing data pipelines and solutions on Databricks.
- Strong proficiency in Python, including libraries for data transformation (e.g., pandas).
- Solid understanding of ETL concepts, data modelling, and pipeline design.
- Experience with Spark and cloud data platforms.
- Ability to document data flows and transformation logic to a high standard.
- Familiarity with project management tools such as Jira.
- Collaborative mindset and strong communication skills.
- Experience in financial services or large enterprise data environments.
- Knowledge of data governance, privacy, and compliance requirements.
- Exposure to business analysis and requirements gathering.
Security Services Data Modelling and Engineering Senior Associate in London employer: Jpmorgan Chase & Co.
Contact Detail:
Jpmorgan Chase & Co. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Security Services Data Modelling and Engineering Senior Associate in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at JPMorganChase. A friendly chat can open doors and give you insider info on what they're really looking for.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines and projects, especially those using Databricks and Python. This will give you an edge and demonstrate your hands-on experience.
✨Tip Number 3
Prepare for the interview by brushing up on your ETL concepts and data modelling knowledge. Be ready to discuss how you've tackled challenges in previous roles, especially around data quality and governance.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're serious about joining the team!
We think you need these skills to ace Security Services Data Modelling and Engineering Senior Associate in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience with Databricks, Python, and data pipelines to show us you’re the right fit for the role.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you’re passionate about data engineering and how your background aligns with our mission at StudySmarter. Be specific about your achievements and how they relate to the role.
Showcase Your Projects: If you've worked on relevant projects, don’t hesitate to include them! We love seeing practical examples of your work, especially those involving data modelling and ETL processes.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Jpmorgan Chase & Co.
✨Know Your Data Pipelines
Make sure you brush up on your experience with data pipelines, especially on Databricks. Be ready to discuss specific projects where you've designed and optimised these pipelines using Python and Spark. This will show that you not only understand the theory but have practical experience too.
✨Collaborate Like a Pro
Since collaboration is key in this role, think of examples where you've worked closely with Data Architects or Business Analysts. Prepare to share how you communicated complex data models and ETL logic clearly, as this will demonstrate your teamwork skills and ability to convey technical information effectively.
✨Showcase Your Documentation Skills
Be prepared to talk about how you document data flows and transformation logic. Bring examples of your documentation work, and explain how it helped maintain transparency and accountability in your previous roles. This will highlight your attention to detail and commitment to best practices.
✨Familiarise Yourself with Governance Standards
Understanding data governance and compliance is crucial for this position. Brush up on relevant standards and be ready to discuss how you've ensured datasets and pipelines meet these requirements in past projects. This will show that you're not just technically skilled but also aware of the broader implications of your work.
