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Consultant | GCP Data Engineer | Ahmedabad | Engineering | Data Modernization & Migration
mid · Consulting
The Team
Deloitte’s Technology & Transformation practice can help you uncover and unlock the value buried deep inside vast amounts of data. Our global network provides strategic guidance and implementation services to help companies manage data from disparate sources and convert it into accurate, actionable information that can support fact-driven decision-making and generate an insight-driven advantage. Our practice addresses the continuum of opportunities in business intelligence & visualization, data management, performance management and next-generation analytics and technologies, including big data, cloud, cognitive and machine learning.
Your work profile
As a GCP Data Engineer Consultant, you will be responsible for designing, developing, and supporting data engineering solutions on Google Cloud Platform (GCP). You will work closely with business stakeholders, architects, and delivery teams to build scalable data pipelines, optimize data workflows, and contribute to successful project delivery.
The role requires strong technical expertise in cloud data engineering, hands-on development experience, and the ability to work in a collaborative consulting environment.
Key Skills & Competencies
Experience
- 3-5 years of overall IT experience.
- Minimum 2+ years of hands-on experience in GCP Data Engineering projects.
- Experience working on cloud-based data integration, transformation, and analytics solutions.
- Exposure to consulting engagements or enterprise-scale implementations is preferred.
Technical Expertise
Google Cloud Platform
Hands-on experience with GCP services including:
- BigQuery
- Dataflow
- Dataproc
- Pub/Sub
- Cloud Storage
- Cloud Composer (Airflow)
Programming & Querying
- Strong proficiency in Python and SQL.
- Experience developing reusable and efficient data processing frameworks.
- Ability to optimize SQL queries and troubleshoot performance bottlenecks.
Data Engineering
- Experience building ETL/ELT pipelines using cloud-native services.
- Knowledge of batch and streaming data processing concepts.
- Understanding of data warehousing and dimensional modeling.
- Familiarity with Spark and distributed data processing frameworks.
DevOps & Engineering Practices
- Experience with Git-based source control.
- Exposure to CI/CD tools and deployment processes.
- Understanding of Agile/Scrum methodologies.
Data Governance & Security
- Basic understanding of data governance principles.
- Knowledge of data quality, security, and access management best practices.
Architecture & Development Responsibilities
- Develop and maintain scalable ETL/ELT pipelines on GCP.
- Build data ingestion and transformation solutions using GCP services.
- Support migration of data workloads from on-premises or legacy platforms to GCP.
- Implement data quality validations and monitoring mechanisms.
- Create and maintain technical documentation for developed solutions.
- Troubleshoot and resolve pipeline failures and performance issues.
- Support implementation of secure and scalable cloud data architectures.
Delivery & Collaboration
- Work closely with stakeholders to understand business and technical requirements.
- Participate in agile ceremonies, sprint planning, and delivery activities.
- Collaborate with architects and senior team members on solution implementation.
- Contribute to project deliverables while ensuring quality and timely completion.
- Support testing, deployment, and production stabilization activities.
Team Contribution
- Participate in code reviews and knowledge-sharing initiatives.
- Follow engineering standards, coding best practices, and governance guidelines.
- Collaborate effectively with cross-functional teams.
- Support onboarding and mentoring of junior team members when required.
Preferred Skills
- Experience with Terraform or Infrastructure as Code
- Exposure to Kubernetes and Docker
- Knowledge of BI tools (Tableau, Power BI, Looker)
Preferred Qualifications
Education – B.Tech / BE or equivalent
Success Metrics
- Delivery of reliable and scalable GCP data engineering solutions.
- Timely completion of assigned project deliverables.
- High-quality ETL/ELT pipeline development and optimization.
- Adherence to engineering, security, and governance standards.
- Effective collaboration with project teams and stakeholders.
- Continuous learning and contribution to GCP practice growth.