Data Engineer III - Python, Databricks

JPMorganChase

Houston (TX)

On-site

USD 100,000 - 140,000

Full time

4 days ago

Be an early applicant

A complete application in a minute

Tailored resume and cover letter, ready to send.

Get past ATS filters

Benefits offered by this job

On-site health and wellness centers

Comprehensive health care coverage

Retirement savings plan

Tuition reimbursement

Mental health support

Job summary

JPMorganChase in Houston seeks a data engineering professional to build secure, scalable data pipelines using Python and Databricks onsite.

You will develop ELT workflows, ensure enterprise data protection, and apply AI-assisted coding practices within governed SDLC processes while maintaining traceability and data sensitivity compliance. Role involves advanced SQL, NoSQL, data modeling updates, and collaboration with security teams to validate AI outputs before production deployment.

Qualifications

Formal training or certification on software engineering concepts and 3 years of applied experience.
Good working knowledge of AWS, Databricks, and Python, with experience across the data lifecycle.
Advanced SQL skills, including joins and aggregations; working understanding of NoSQL databases.
Significant experience with statistical data analysis and the ability to select appropriate tools and data patterns.
Ability to utilize AWS Cloud Services to develop, deploy, and manage applications at scale.
Proficiency with AI coding assistants, using tools daily such as Cursor, GitHub Copilot, and Claude for code generation, documentation, and refactoring.
Prompt engineering skills: provide context, clear goals, relevant source material, and defined output expectations to produce accurate, usable code.
Critical evaluation and validation to identify hallucination patterns, security vulnerabilities, and logic errors in AI-generated code before production deployment.
Demonstrated experience using enterprise-authorized AI capabilities to support data engineering workflows with strong validation habits and awareness of data sensitivity.

Responsibilities

Develop ELT workflows using Python and Databricks.
Support review of controls to ensure protection of enterprise data.
Implement data security using entitlements frameworks.
Update data models as new use cases emerge.
Use SQL frequently and apply NoSQL knowledge.
Apply AI capabilities to accelerate data pipeline design and analysis/documentation.
Validate AI-assisted outputs and handle data according to sensitivity and security requirements.
Adopt reuse-first, AI-assisted SDLC practices including test generation and control validation.
Ensure traceability and alignment to resiliency and security expectations.

Skills

Python

Databricks

SQL

NoSQL

AWS

Cursor

GitHub Copilot

Claude

AI assistants

Prompt engineering

Education

Software engineering certification

Tools

Docker

CI/CD

Aurora Postgres

MongoDB

Job description

Build secure, scalable data pipelines and analytics foundations using Python and Databricks in an onsite Houston environment.

Responsibilities

Develop workflows and ELT pipelines using Python and Databricks.
Support review of controls to ensure sufficient protection of enterprise data.
Implement data security using entitlements frameworks.
Update logical or physical data models as new use cases emerge.
Use SQL frequently and apply working knowledge of NoSQL databases.
Apply enterprise-authorized AI capabilities within the work environment to accelerate data pipeline and design analysis/documentation.
Validate AI-assisted outputs and handle data according to sensitivity and security requirements.
Use reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (including test generation and control validation).
Ensure traceability/auditability and alignment to resiliency and security expectations.

Requirements

Formal training or certification on software engineering concepts and 3 years of applied experience.
Good working knowledge of AWS, Databricks, and Python, with experience across the data lifecycle.
Advanced SQL skills, including joins and aggregations; working understanding of NoSQL databases.
Significant experience with statistical data analysis and the ability to select appropriate tools and data patterns.
Ability to utilize AWS Cloud Services to develop, deploy, and manage applications at scale.
Proficiency with AI coding assistants, using tools daily such as Cursor, GitHub Copilot, and Claude for code generation, documentation, and refactoring.
Prompt engineering skills: provide context, clear goals, relevant source material, and defined output expectations to produce accurate, usable code.
Critical evaluation and validation to identify hallucination patterns, security vulnerabilities, and logic errors in AI-generated code before production deployment.
Demonstrated experience using enterprise-authorized AI capabilities to support data engineering workflows with strong validation habits and awareness of data sensitivity.
Ability to review and validate AI-assisted outputs (query suggestions, test ideas, model change summaries) before use; escalate when uncertain and follow data handling requirements.

Preferred Qualifications, Capabilities, and Skills

Familiarity with Standardized data layer practices, including Medallion architecture.
Exposure to Aurora Postgres and MongoDB.
Experience developing and supporting AWS GLUE Jobs and Federated Data Lake.
Skills in designing efficient data models, including normalization, denormalization, and schema design, with understanding of relational and star schemas.
Augmented development workflow: integrating tools into CI/CD pipelines, using containerization (for example, Docker), and leveraging AI to bridge language gaps (for example, transitioning between Python, JavaScript, or Java).