Data Engineer III
On-site · London, England, United Kingdom
Job Summary
Data Engineer III at JPMorgan Chase (Corporate and Investment Bank Payments Technology): lead the design, development, and maintenance of scalable data pipelines and architectures to collect, store, access, and analyze data securely and reliably. Own end-to-end data pipeline development, define and maintain data models, ensure data quality, and enable secure data movement across internal and external systems. Collaborate with data scientists, analysts, and business stakeholders to extract actionable insights, support predictive models for cash forecasting and fund movement, and deliver dashboards and visualizations using Tableau. Leverage modern technologies (Python, SQL, Spark, Flink, Airflow, Databricks) across cloud platforms (AWS/Azure/GCP), implement data governance, and document data designs, lineage, and assumptions for technical and non-technical audiences.
Required Qualifications
- Experience in data engineering or a closely related field
- Proven experience designing and building scalable data pipelines (ETL/ELT) for batch and streaming use cases using modern technologies (e.g., Python, SQL, Spark, Flink, Airflow) and advanced knowledge on the workings of these
- Hands-on experience in building and operating workloads Databricks
- Hands-on experience creating data visualizations and dashboards in Tableau
- Familiarity with cloud data platforms (e.g., AWS, Azure, GCP) and big data technologies
- Strong statistical analysis skills with the ability to interpret complex data, determine appropriate tooling, build performant datasets, and deliver business value
- Experience integrating data from multiple sources and systems (APIs, databases, files, streaming), including data modeling and orchestration best practices
- Ability to work independently and collaboratively in a fast-paced, agile environment, partnering with data scientists, analysts, software engineers and business stakeholders
- Excellent communication and documentation skills, including clear articulation of data designs, lineage, and assumptions for technical and non-technical audiences
- Experience with financial concepts, especially in banking, treasury and cash management
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