Data Engineer
Summary
Designs and maintains scalable data pipelines and warehouses on GCP/BigQuery using Python, SQL, and Airflow to deliver clean, high-quality data for analytics and reporting.
Data Engineer
Hybrid; Jersey City, NJ
$80k-$95k plus bonus and benefits
EEO/Minorities/Females/Vets/Disabilities
For more information on benefits and what we offer please visit us at
The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.
We are looking for a skilled Data Engineer with strong experience in building, optimizing, and maintaining scalable data platforms and pipelines. The ideal candidate will work closely with data scientists, analysts, and business teams to ensure reliable, high-quality data delivery across analytics and reporting use cases.
Key Skills & Technologies
- Strong programming experience in Python and SQL
- Hands-on experience with Google Cloud Platform (GCP) services
- Expertise in BigQuery for data warehousing, performance tuning, and cost optimization
- Experience with ETL/ELT frameworks and large-scale data pipeline development
- Workflow orchestration using Apache Airflow
- CI/CD implementation for data pipelines using tools like Git, Jenkins, or Cloud Build
- Solid understanding of data modeling, partitioning, and schema design
- Experience with cloud storage, data validation, and monitoring
- Knowledge of containerization (Docker) and basic DevOps practices is a plus
- Design, develop, and maintain scalable and reliable data pipelines
- Build and optimize ETL/ELT processes to ingest data from multiple sources
- Develop and manage data models in BigQuery to support analytics and reporting
- Implement automated workflows and scheduling using Airflow
- Ensure data quality, integrity, and performance across pipelines
- Collaborate with cross-functional teams to gather requirements and deliver data solutions
- Apply CI/CD best practices to support efficient and reliable deployments
- Troubleshoot and resolve data pipeline and performance issues
Graduate in Data Science, Computer Science, Statistics, or a related field. 3-4 years of experience in data science or data analysis.