Data Quality Engineer - Contractor
New
J
JupiterClimate Technology
All - San Mateo, CA | Boulder, CO | New York, NYContractMiddle
Salary55 - 65 USD per hour
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Job Details
- Experience
- 3+ years
- Required Skills
- AWSDockerPythonSQLETLGitPandas
Requirements
- 3+ years of experience in data engineering, data quality, or analytics engineering roles.
- Bachelor’s degree in computer science, statistics, or a related field, or equivalent practical experience.
- Proficiency in Python coding for production systems (modular code, not jupyter notebooks).
- Experience with Git workflow and collaborative code review.
- Knowledge of Docker images and container build tooling.
- Experience with data validation and verification pipeline design, including automated testing.
- Proficiency with data exploration and engineering tools such as pandas and SQL.
- Experience with DAG-based ETL orchestration (e.g., Prefect).
- Experience with AWS, specifically S3.
- Experience managing or coordinating with external data vendors, including data quality troubleshooting.
- Experience working in a fast-paced, cross-functional environment, ideally supporting a product launch.
- Experience partnering with quantitative or financial roles.
Responsibilities
- Design and execute frameworks for data quality assurance and validation.
- Collaborate across the organization to implement new features, support internal stakeholders, and investigate and improve data infrastructure.
- Partner with the Sr. Engineer, Quantitative Modeler, and Solutions Architect to ensure data feeding the product and financial models is validated and launch-ready.
- Participate in stand-ups and other milestone meetings to maintain momentum and alignment across the team.
- Serve as the data quality checkpoint between vendor data ingestion and product delivery, catching issues before they surface downstream.
- Establish data quality monitoring and documentation to support the pipeline post-launch.
- Surface data quality risks and vendor issues to the Director of Engineering.
- Manage external data vendor relationships and ensure ongoing data quality and validation.
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