Data Engineering Architect (GCP)

Job Title:

Location:

About the Role

Data Engineering Architect

Key Responsibilities

Design & Architecture:

• Design and implement scalable, reliable, and efficient data architectures for Google products and services.
  
• Develop and maintain

  data models, schemas, and ontologies

  to support diverse data sources.
• Evaluate and recommend

  emerging data technologies

  to enhance the data infrastructure.
• Collaborate with

  product managers, engineers, and researchers

  to define data requirements and translate them into technical solutions.

Data Processing & Pipelines:

• Build and optimize

  batch and real-time data pipelines

  using GCP services such as Dataflow, Dataproc, Pub/Sub, and Cloud Functions.
• Implement

  data quality checks and validation processes

  to ensure data accuracy and consistency.
• Define and enforce

  data governance policies

  for security and compliance.

Data Storage & Management:

• Architect scalable data storage solutions using

  BigQuery, Cloud Storage, and Spanner

  .
• Optimize data storage and retrieval for performance and cost-effectiveness.
  
• Implement

  data lifecycle management policies

  including retention, archiving, and deletion.

Team Leadership & Mentorship:

• Provide

  technical guidance

  to data engineers and team members.
• Mentor junior engineers to build expertise in data engineering best practices.
  
• Foster a culture of

  innovation, collaboration, and continuous learning

  within the team.

Required Qualifications

• 6–8 years of experience in

  data engineering or related fields

  .
• Strong understanding of

  data warehousing, data modeling, and ETL/ELT processes

  .
• Proven expertise in designing and implementing

  large-scale data pipelines and architectures

  .
• Proficiency in

  SQL

  and at least one programming language (

  Python or Java

  ).
• Hands-on experience with

  GCP services

  : BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Spanner.
• Experience with

  big data frameworks

  : Hadoop, Spark, Kafka.
• Excellent

  communication, collaboration, and problem-solving skills

  .

Preferred Qualifications

• Experience in

  data governance and quality management

  .
• Familiarity with

  machine learning and data science

  workflows.
• Knowledge of

  Docker, Kubernetes, and cloud-based orchestration

  .
• Contributions to

  open-source projects

  or communities.

• Google Cloud Professional Data Engineer certification

  .

Technical Skills Summary

Mandatory:

• GCP (BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage)
  
• SQL & Python/Java
  
• Data modeling, ETL/ELT, data warehousing
  
• Batch & real-time data pipelines
  
• Big data frameworks: Hadoop, Spark, Kafka
  

Preferred:

• Data governance and quality management
  
• Machine learning & data science pipelines
  
• Docker & Kubernetes