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
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