Gcp Data Engineer

GCP Data Engineers

Roles & Responsibilities

1. Design & Development of Data Solutions

• Build, optimize, and maintain large-scale data pipelines using

  GCP BigQuery

  and cloud-native services.
• Develop efficient Python-based data processing scripts and reusable components.
  
• Write complex SQL queries for data transformation, analysis, and validation at scale.
  

2. Data Processing & Large-Volume Handling

• Work with structured, semi-structured, and unstructured data across high-volume environments.
  
• Implement efficient ETL/ELT workflows for batch and real-time processing.
  
• Apply best practices for performance tuning, query optimization, and cost efficiency in BigQuery.
  

3. Cloud Integration & Tooling

• Integrate datasets across GCP services such as

  Cloud Storage, Cloud Functions, Pub/Sub, Dataflow, Dataproc

  , etc.
• Build robust data ingestion pipelines from APIs, databases, and streaming sources.
  
• Leverage GCP-native security, monitoring, and logging tools to ensure reliability and compliance.
  

4. Technical Leadership (Lead Role Only)

• Lead end-to-end design discussions, architecture planning, and technical code reviews.
  
• Mentor and guide junior engineers with best practices in GCP and data engineering.
  
• Collaborate with cross-functional teams to define data strategy, standards, and governance.
  

5. Problem Solving & Optimization

• Troubleshoot complex data pipeline issues, bottlenecks, and failures.
  
• Identify opportunities for automation, optimization, and performance improvements.
  
• Ensure scalability, stability, and maintainability of data solutions.
  

6. Stakeholder Collaboration

• Work closely with business analysts, product owners, and data scientists to understand requirements.
  
• Translate business needs into high-quality technical solutions.
  
• Prepare technical documentation, design diagrams, and operational guidelines.
  

Requirements

Technical Skills

• Strong hands-on experience with

  GCP BigQuery

  (data modeling, optimization, partitioning, clustering).
• Minimum

  3+ years

  solid experience in

  Python

  for data engineering.
• Advanced

  SQL

  skills for querying and transforming large datasets.
• Experience with cloud-native tools (Cloud Composer, Pub/Sub, Dataflow, Cloud Functions, etc.).
  

Professional Skills

• Strong analytical and problem-solving skills.
  
• Ability to work in fast-paced client environments.
  
• Excellent communication and stakeholder management abilities.