Gravity IT Resourcing
Data Platform Engineer
/, Florida
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Job Title: Data Platform Engineer
Location: Remote
Work Authorization: USC/GC Holder/H1B
Job Type: 4-5 month contract to hire
Position Overview:
We are seeking a skilled Data Platform Engineer with a strong focus on pipeline development and data integration. The ideal candidate will have experience designing, building, and maintaining data pipelines, managing data warehouses, and implementing APIs for real-time data access. This role requires a deep understanding of data infrastructure and a commitment to delivering high-quality solutions.
Responsibilities:
- Pipeline Development: Design, develop, and maintain data pipelines to efficiently move data in and out of the data warehouse.
- Data Warehouse Management: Oversee the management of the data warehouse, ensuring optimal performance and reliability.
- Real-Time Data Access: Implement and manage APIs to provide real-time data access for internal use cases, handling private and sensitive information.
- Automation & Scripting: Use Python to automate and script data pipeline tasks, perform complex data transformations and cleansing, orchestrate workflows, and troubleshoot pipeline issues.
- Data Transformation & Querying: Utilize SQL for data cleansing, transformation, and querying within large datasets.
- Database Management: Manage relational databases for storing, managing, and querying large datasets, design schemas, and enable efficient data warehousing operations to support data movement in and out of pipelines.
- Maintenance & Support: Provide support for existing data pipelines and systems, addressing issues and performing necessary maintenance.
Qualifications:
- Experience: 3-7 years of experience in data engineering, with a focus on pipeline development, data warehousing, and API integration.
- Technical Skills:
- Python for automating and scripting data pipeline tasks, performing complex data transformations and cleansing, orchestrating workflows with tools, and troubleshooting pipeline issues.
- SQL for data cleansing, transformation, and querying within large datasets.
- Relational Databases for storing, managing, and querying large datasets, designing schemas, and enabling efficient data warehousing operations to support the movement of data in and out of pipelines.
- Proficiency in AWS services (e.g., Redshift, DynamoDB)
- Experience with data pipeline tools (e.g., Prefect, Airbyte, DBT)