Google Cloud Data Engineer
New
J
JobgetherData engineering
Work remotely from India, Approximately six hours of overlap with North American Eastern TimeFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Between 3 and 10 years of experience in data engineering
- Required Skills
- PythonSQLGCPData engineeringData modeling
Requirements
- Have 3 to 10 years of experience in data engineering.
- Have hands-on experience designing, building, and maintaining data solutions on Google Cloud.
- Demonstrate strong SQL proficiency, including complex queries, transformations, and performance tuning.
- Have advanced Python skills for pipelines, processing logic, workflow automation, and maintainable engineering solutions.
- Understand data ingestion, transformation, pipeline orchestration, data modeling, and distributed processing.
- Have experience designing pipelines that account for late-arriving data, duplicates, incomplete datasets, and inconsistent source information.
- Understand data quality, governance, security, compliance, data residency, and sensitive-information protection.
- Be familiar with cloud networking, access management, and security controls.
- Be able to investigate data issues and balance processing efficiency, reliability, and infrastructure costs.
- Be available for approximately six hours of overlap with North American Eastern Time.
- A Professional Google Cloud certification is a plus.
- Experience supporting enterprise clients, regulated industries, or large-scale data platforms is an advantage.
Responsibilities
- Design and maintain scalable data platforms on Google Cloud.
- Build, orchestrate, and optimize pipelines for data ingestion, transformation, validation, and delivery.
- Develop complex SQL queries and Python-based processing solutions.
- Create resilient workflows that handle late-arriving, duplicated, incomplete, and inconsistent data.
- Develop data models and architectures that support clarity, consistency, and performance as data grows.
- Optimize processing performance, resource use, and cloud infrastructure costs.
- Implement data quality checks, governance practices, validation, and monitoring.
- Apply cloud networking, security, access control, and data protection practices.
- Collaborate with clients, Google Cloud teams, and technical stakeholders on requirements and architectural trade-offs.
- Troubleshoot production data systems and own delivery from design through deployment, maintenance, and optimization.
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