Data Engineer+ GCP

Requirements

• 5+ years of experience in data engineering, data architecture, or related roles
• Proven track record deploying large-scale data pipelines on GCP
• Hands-on experience with enterprise data solutions, data lakes, or data warehouses
• Demonstrated ability to work collaboratively with cross-functional teams
• Experience with real-time data integration and processing workflows preferred
• Day-to-Day Activities
• Design, develop, and maintain scalable data pipelines and workflows on GCP
• Implement, optimize, and troubleshoot batch and streaming data processes
• Collaborate with data analysts, scientists, and stakeholders to understand data requirements
• Monitor workflows, troubleshoot data pipeline issues, and implement improvements
• Ensure data security, privacy, and compliance across all environments
• Automate deployment and management of data infrastructure using DevOps tools
• Document technical architecture, processes, and standards
• Keep abreast of GCP innovations and incorporate relevant new features into solutions
• Qualifications & Soft Skills
• Bachelor's degree in Computer Science, Data Science, Engineering, or related field
• 5+ years of experience in data engineering, big data, or cloud data solutions
• Relevant certifications such as Google Professional Data Engineer are advantageous
• Soft Skills:
• Strong analytical, problem-solving, and organizational skills
• Effective communication skills for collaborating with technical and non-technical teams
• Ability to work independently and as part of a team
• Detail-oriented, with a focus on quality and accuracy
• Adaptability and eagerness to learn emerging cloud and data technologies
• Proactive attitude with a focus on continuous improvemen
• S YNECHRON'S DIVERSITY & INCLUSION STATEMENT
• All employment decisions at Synechron are based on business needs, job require

Benefits

Additional Information

Overall Responsibilities Design, develop, and maintain scalable data pipelines and workflows using GCP services Build and optimize data models, schemas, and warehouses (BigQuery, Cloud SQL, etc.) aligned with business needs Collaborate with data analysts, data scientists, and business stakeholders to gather requirements and deliver effective solutions Ensure data quality, security, and compliance across all data environments Automate data workflows, monitor system health, and troubleshoot issues proactively Optimize infrastructure for performance and cost-efficiency within GCP Document architecture, processes, and best practices for data engineering solutions Stay current with GCP advancements and incorporate new features and tools for continuous improvement Software Requirements Proficiency in SQL and programming languages such as Python, Java, or Scala for data processing Hands-on experience with GCP data services including BigQuery, Cloud Dataflow, Cloud Data Fusion, Cloud Storage, and Dataproc Knowledge of ETL/ELT processes, data modeling, and data warehousing concepts Experience designing and implementing scalable and secure data pipelines on GCP Familiarity with containerization (Docker) and orchestration (GKE, Kubernetes) Understanding of data security, encryption, and compliance policies in cloud environments Experience with Apache Spark, Kafka, or Pub/Sub for real-time data streaming Knowledge of machine learning workflows on GCP (Vertex AI, AI Platform) Exposure to Cloud Identity & Access Management (IAM) and cybersecurity best practices Category-wise Technical Skills Data Processing & Frameworks: GCP data services: BigQuery, Dataflow, Dataproc, Data Fusion, Cloud Storage Programming languages: Python, Java, Scala Real-time streaming: Pub/Sub, Kafka (preferred) Batch processing frameworks: Apache Spark, Dataflow Cloud & Infrastructure: GCP Identity & Access Management (IAM), VPC, Cloud Networking Deployment automation: Terraform, Deployment Manager Container orchestration: Google Kubernetes Engine (GKE) Data Modeling & Warehousing: Designing schemas and aggregations for analytical processing Data governance, access controls, lineage, and audit trails Tools & Monitoring: Stackdriver, Data Studio, Looker, or Power BI for visualization and monitoring Version control: Git, CI/CD integrations

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