Senior Data Engineer: Scalable Pipelines & Insights

Testing Site - JPMC; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

JPMorgan Chase is seeking a Data Engineer III within Corporate and Investment Bank Payments Technology. You will design, build, and maintain scalable data pipelines and architectures across diverse business functions to support data collection, storage, and analytics.

You will work with Python, SQL, Spark, and Airflow, enabling trusted data flows, data modeling, and data-driven insights for cash forecasting and fund movement. Strong collaboration with data scientists and stakeholders is required.

Qualifications

• Experience designing and building scalable data pipelines (ETL/ELT) for batch and streaming use cases.
• Hands-on experience with Databricks.
• Proficiency in Tableau for data visualizations and dashboards.
• Strong cloud data platform knowledge (AWS/Azure/GCP).

Responsibilities

• Own end-to-end data pipeline development and ensure data protection controls.
• Develop scalable data pipelines for data collection, transformation, and delivery across systems.
• Define and maintain data models for new use cases.
• Ensure data quality, reliability, and timeliness across pipelines.
• Collaborate with product, engineering, and business teams to deliver solutions.

Skills

Data engineering

ETL/ELT pipelines

Python

SQL

Spark

Flink

Airflow

Databricks

Tableau

AWS

Azure

GCP

Data modeling

APIs

Data quality

Education

Bachelor's degree in Computer Science or related field

Tools

Databricks

Tableau

Airflow

Spark

Flink

Git

Job description

JPMorgan Chase is seeking a Data Engineer III within Corporate and Investment Bank Payments Technology. You will design, build, and maintain scalable data pipelines and architectures across diverse business functions to support data collection, storage, and analytics.

You will work with Python, SQL, Spark, and Airflow, enabling trusted data flows, data modeling, and data-driven insights for cash forecasting and fund movement. Strong collaboration with data scientists and stakeholders is required.