Note: The job is a remote job and is open to candidates in USA. Dice is seeking a Senior Data Engineer with expertise in Google Cloud Platform to lead the development of their enterprise data ecosystem. The role involves designing and deploying advanced data architectures and ensuring data integrity, scalability, and cost-efficiency across global data assets.
Responsibilities
- Architectural Strategy & System Design
- Enterprise Framework Design: Conceptualize and implement end-to-end data architectures utilizing Google Cloud Platform s Modern Data Stack (BigQuery, Dataflow, Pub/Sub)
- Scalable Data Modeling: Lead the development of high-performance data models (Star, Snowflake, Data Vault) optimized for multi-petabyte scale and high-concurrency analytics
- Hybrid & Multi-Cloud Strategy: Provide technical leadership on data integration strategies spanning Google Cloud Platform, on-premise systems, and third-party SaaS environments
- Advanced Engineering & Pipeline Automation
- Distributed Processing: Engineer highly resilient, low-latency streaming and batch pipelines using Apache Beam (Dataflow) and Cloud Composer (Airflow)
- Software Engineering Excellence: Develop reusable Python libraries and frameworks to standardize data ingestion, logging, and error-handling across the engineering team
- Infrastructure as Code (IaC): Drive operational maturity by managing cloud resources exclusively through Terraform, ensuring robust versioning and environment parity
- Data Governance, Security & Performance
- System Optimization: Conduct deep-dive performance tuning of BigQuery environments, implementing partitioning, clustering, and slot management to optimize ROI
- Security & Compliance: Architect data security protocols including VPC Service Controls, IAM Least Privilege, and data masking/encryption to meet global compliance standards (GDPR, SOC2)
- Observability: Establish comprehensive monitoring and alerting frameworks for data health, ensuring high availability and meeting stringent Service Level Objectives (SLOs)
- Technical Leadership & Collaboration
- Strategic Mentorship: Serve as a mentor to mid-level and junior engineers, conducting rigorous code reviews and promoting best practices in DataOps
- Stakeholder Alignment: Act as a primary technical liaison between Data Science, Business Intelligence, and Executive leadership to translate business goals into technical roadmaps
Skills
- 8-10 years of professional experience in data engineering
- Mastery of distributed computing
- Advanced Python development skills
- Expert-level SQL optimization skills
- Experience with Google Cloud Platform's Modern Data Stack (BigQuery, Dataflow, Pub/Sub)
- Ability to conceptualize and implement end-to-end data architectures
- Experience in developing high-performance data models (Star, Snowflake, Data Vault)
- Technical leadership on data integration strategies spanning Google Cloud Platform, on-premise systems, and third-party SaaS environments
- Experience in engineering resilient, low-latency streaming and batch pipelines using Apache Beam (Dataflow) and Cloud Composer (Airflow)
- Ability to develop reusable Python libraries and frameworks for data ingestion, logging, and error-handling
- Experience managing cloud resources through Terraform
- Conducting performance tuning of BigQuery environments
- Architecting data security protocols including VPC Service Controls, IAM Least Privilege, and data masking/encryption
- Establishing monitoring and alerting frameworks for data health
- Mentoring mid-level and junior engineers
- Conducting code reviews and promoting best practices in DataOps
- Acting as a primary technical liaison between Data Science, Business Intelligence, and Executive leadership
- Bachelors or Masters in Information Technology, Computer Science or relevant field
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