Lead Enterprise Architect

Job Description:

Role Overview:
We are seeking a highly skilled and motivated Cloud Data Engineering Manager to join our team. The role is critical to the development of a cutting-edge reporting platform designed to measure and optimize online marketing campaigns.

The GCP Data Engineering Manager will design, implement, and maintain scalable, reliable, and efficient data solutions on Google Cloud Platform (GCP). The role focuses on enabling data-driven decision-making by developing ETL/ELT pipelines, managing large-scale datasets, and optimizing data workflows. The ideal candidate is a proactive problem-solver with strong technical expertise in GCP, a passion for data engineering, and a commitment to delivering high-quality solutions aligned with business needs.

Key Responsibilities:

Data Engineering & Development:

• Design, build, and maintain scalable ETL/ELT pipelines for ingesting, processing, and transforming structured and unstructured data.
• Implement enterprise-level data solutions using GCP services such as BigQuery, Dataform, Cloud Storage, Dataflow, Cloud Functions, Cloud Pub/Sub, and Cloud Composer.
• Develop and optimize data architectures that support real-time and batch data processing.
• Build, optimize, and maintain CI/CD pipelines using tools like Jenkins, GitLab, or Google Cloud Build.
• Automate testing, integration, and deployment processes to ensure fast and reliable software delivery.

Cloud Infrastructure Management:

• Manage and deploy GCP infrastructure components to enable seamless data workflows.
• Ensure data solutions are robust, scalable, and cost-effective, leveraging GCP best practices.

Infrastructure Automation and Management:

• Design, deploy, and maintain scalable and secure infrastructure on GCP.
• Implement Infrastructure as Code (IaC) using tools like Terraform.
• Manage Kubernetes clusters (GKE) for containerized workloads.

Collaboration and Stakeholder Engagement:

• Work closely with cross-functional teams, including data analysts, data scientists, DevOps, and business stakeholders, to deliver data projects aligned with business goals.
• Translate business requirements into scalable, technical solutions while collaborating with team members to ensure successful implementation.

Quality Assurance & Optimization:

• Implement best practices for data governance, security, and privacy, ensuring compliance with organizational policies and regulations.
• Conduct thorough quality assurance, including testing and validation, to ensure the accuracy and reliability of data pipelines.
• Monitor and optimize pipeline performance to meet SLAs and minimize operational costs.

Qualifications and Certifications:

• Education:
  • Bachelor’s or master’s degree in computer science, Information Technology, Engineering, or a related field.
• Experience:
  • Minimum of 7 to 9 years of experience in data engineering, with at least 4 years working on GCP cloud platforms.
  • Proven experience designing and implementing data workflows using GCP services like BigQuery, Dataform Cloud Dataflow, Cloud Pub/Sub, and Cloud Composer.
• Certifications:
  • Google Cloud Professional Data Engineer certification preferred.

Key Skills:

• Mandatory Skills:
  • Advanced proficiency in Python for data pipelines and automation.
  • Strong SQL skills for querying, transforming, and analyzing large datasets.
  • Strong hands-on experience with GCP services, including Cloud Storage, Dataflow, Cloud Pub/Sub, Cloud SQL, BigQuery, Dataform, Compute Engine and Kubernetes Engine (GKE).
  • Hands-on experience with CI/CD tools such as Jenkins, GitHub or Bitbucket.
  • Proficiency in Docker, Kubernetes, Terraform or Ansible for containerization, orchestration, and infrastructure as code (IaC)
  • Familiarity with workflow orchestration tools like Apache Airflow or Cloud Composer
  • Strong understanding of Agile/Scrum methodologies
• Nice-to-Have Skills:
  • Experience with other cloud platforms like AWS or Azure.
  • Knowledge of data visualization tools (e.g., Power BI, Looker, Tableau).
  • Understanding of machine learning workflows and their integration with data pipelines.

Soft Skills:

• Strong problem-solving and critical-thinking abilities.
• Excellent communication skills to collaborate with technical and non-technical stakeholders.
• Proactive attitude towards innovation and learning.
• Ability to work independently and as part of a collaborative team.

Location:

Brand:

Time Type:

Contract Type: