DataOps Engineer at Eeze focusing on data pipeline stability across multiple products. Collaborating with IT teams to maintain quality, observability, and operational efficiency.
About the role
- GCP Data Engineer responsible for building and optimizing scalable data pipelines using GCP services. Develop, maintain, and ensure data quality in ETL/ELT workflows with Python and SQL.
Responsibilities
- Develop, and maintain scalable data pipelines using GCP services
- Build and optimize ETL/ELT workflows using Dataflow and BigQuery
- Orchestrate workflows using Cloud Composer (Apache Airflow)
- Perform data migration from legacy systems (e.g., Teradata, on-prem databases) to GCP
- Develop reusable and efficient Python-based data processing frameworks
- Write optimized and complex SQL queries for data transformation and analytics
- Leverage AI-native engineering tools (e.g., code assistants, automated testing, query optimization tools) to improve engineering throughput
- Ensure data quality, validation, and governance compliance
- Monitor and troubleshoot data pipelines and production issues
- Optimize pipelines for performance, scalability, and cost efficiency
Requirements
- Overall 6-15+ years with 3-5 years of relevant work experience in GCP Services
- B.Tech., M.Tech. or MCA degree from a reputed university
- The candidate must demonstrate proficiency in, Strong hands-on experience in Google Cloud Platform (GCP) – BigQuery, Dataflow, Cloud Composer
- Proficiency in Python for data processing
- Advanced knowledge of SQL (joins, window functions, performance tuning)
- Experience in ETL/ELT pipeline development and migration to cloud
- Understanding of data warehousing and data modeling concepts
- Experience working with large-scale distributed data systems
- Nice-to-have skills
- Knowledge in Pyspark/Dataproc
- Knowledge in Linux scripting
- Knowledge on Github, Jenkins, Jira, etc.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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