Job description
Job Title: Senior Data Engineer
Location: Remote/Hybrid
Experience: Minimum 4 years total, including 2+ years in GCP and 1+ year in DBT
Role Summary
We are seeking a Senior Data Engineer with expertise in building scalable and reliable data pipelines, particularly on Google Cloud Platform (GCP). The ideal candidate has hands-on experience with DBT, strong SQL skills, and a deep understanding of data transformation, modeling, and orchestration.
You will play a key role in architecting and maintaining robust data workflows that enable business intelligence and analytics teams to access clean, well-modeled data in a timely and efficient manner.
Key Responsibilities
Design, develop, and maintain ETL/ELT pipelines on Google Cloud Platform
Use DBT to transform and manage clean, modular, and reusable data models
Collaborate with business stakeholders to define and refine data requirements
Leverage Apache Airflow or Cloud Composer for workflow orchestration
Work with various cloud data warehouses such as BigQuery, Snowflake, or Redshift
Optimize data workflows for performance, reliability, and cost-effectiveness
Ensure high standards in documentation, testing, and version control
Contribute to and promote team best practices, code reviews, and continuous improvement
Required Skills & Experience
4+ years in data engineering or a related role
2+ years of experience working with GCP services (e.g., BigQuery, GCS, Dataflow)
1+ year of hands-on experience with DBT
Proficient in SQL and data modeling concepts
Experience with orchestration tools like Apache Airflow or Cloud Composer
Strong understanding of data pipelines, modularity, and maintainability
Ability to gather business requirements and translate them into technical solutions
Exposure to Apache Spark, Kafka, or other big data/streaming tools (preferred)
Preferred Qualifications
GCP certification (Associate Cloud Engineer or higher)
Experience working in multi-cloud environments (GCP + AWS)
Familiarity with CI/CD pipelines, version control systems like Git, and DevOps practices in data projects
