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Description supplied by the original job listing.
Make your mark building trusted data products on Databricks—grow your engineering skills while enabling insights and AI solutions across Securities Services.
As a Data Engineer in the Securities Services Data Modelling and Engineering team within AI Transformation, you will build the data foundation that powers business insights, objectives and key results tracking, and AI enablement across JPMorganChase’s Securities Services businesses. You will design and develop scalable data pipelines and reusable datasets on Databricks using Python and Spark. You will collaborate 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 using Python and Spark.
Collaborate with Data Architects and Business Analysts to develop robust data models aligned to business needs.
Document data flows, extract-transform-load logic, and transformation rules to a high standard.
Implement data quality checks and validation modules using Python.
Execute testing and validation processes to support accurate and reliable datasets.
Track work and progress in Jira to maintain transparency and accountability.
Register datasets and pipelines in relevant catalogues and consoles in line with governance and privacy standards.
Required qualifications, skills, and capabilities:
Proven experience developing data pipelines and solutions on Databricks.
Proficiency in Python for data transformation, including libraries such as pandas.
Understanding of extract-transform-load concepts, data modelling, and pipeline design.
Experience using Spark.
Experience with cloud data platforms.
Ability to document data flows and transformation logic clearly and consistently.
Experience using Jira for project tracking.
Ability to collaborate effectively with Data Architects and Business Analysts.
Strong communication skills.
Preferred qualifications, skills, and capabilities:
Experience in financial services or large enterprise data environments.
Knowledge of data governance, privacy, and compliance requirements.
Exposure to business analysis and requirements gathering.
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