Data Engineer designing and building data pipelines for PG&E's reliability initiatives. Engaging in continuous improvement and collaborating with various stakeholders in hybrid role.
About the role
- Full Stack Data Engineer responsible for building and optimizing data pipelines and lakehouse architectures. Collaborating with teams to ensure data quality and delivering analytics-ready datasets.
Responsibilities
- Design, develop, and maintain end-to-end data pipelines using **Python, PySpark, and SQL**
- Build and optimize data transformation workflows using **dbt on Snowflake**
- Develop scalable **lakehouse architectures** for structured and semi-structured data
- Implement reliable data ingestion frameworks using **Kafka, AWS Glue, and custom connectors**
- Orchestrate workflows and manage dependencies using **Apache Airflow**
- Manage cloud infrastructure on **AWS** (S3, Glue, EMR, Redshift/Snowflake integrations)
- Implement Infrastructure as Code (IaC) using **Terraform**
- Collaborate with cross-functional teams to deliver analytics-ready datasets
- Ensure data quality, performance optimization, and cost efficiency
- Use **GitLab** for version control, CI/CD, and collaborative development
- Monitor, troubleshoot, and resolve data pipeline issues in production environments
Requirements
- 4+ years of experience with **AWS** data services and cloud-based data engineering
- Strong programming skills in **Python** and **PySpark**
- Hands-on experience with **Snowflake** and **dbt** for data modeling and transformations
- Solid understanding of **SQL** for complex analytical queries
- Experience with **Apache Airflow** for workflow orchestration
- Proficiency in **Kafka** for real-time/streaming data ingestion
- Experience with **AWS Glue/ Airflow** for ETL development
- Experience with **Terraform** for infrastructure automation
- Strong experience with **GitLab** and CI/CD pipelines
Job title
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Experience level
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Not specified
Degree requirement
Tech skills
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