Match Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessLog in to see why each signal reads the way it does.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, deploy, and maintain scalable batch and real-time data pipelines using Google Cloud Platform services such as BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Composer.
Ensure production data pipelines meet requirements for performance, reliability, data quality, security, and cost optimization while implementing monitoring, alerting, and operational support practices.
Collaborate cross-functionally with architects, analysts, ML engineers, and business stakeholders, and mentor junior engineers while contributing to reusable frameworks and best practices.
Minimum Requirements
Strong hands-on expertise with Google Cloud Platform data engineering stack including BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, and Cloud Composer.
Proficient programming skills in Python and advanced SQL; experience with Apache Beam and/or Apache Spark for data processing.
Experience building both batch and streaming data pipelines in production environments.
Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced with design and optimization of data models, ETL/ELT frameworks, and data ingestion from diverse enterprise sources in GCP environment.
Familiar with GCP security best practices, CI/CD pipelines for data workloads, Infrastructure-as-Code (preferably Terraform), containerization, and monitoring/observability tools.
Able to operate in a complex, enterprise-scale cloud data environment, working on highly available, fault-tolerant, and cost-optimized data engineering solutions.