About the Role
We are looking for a highly skilled Data Engineer with strong Google Cloud Platform (GCP) expertise to design, develop, and maintain scalable data solutions. The ideal candidate will have hands-on experience in building robust ETL/ELT frameworks, real-time and batch data pipelines, data modeling, and leveraging modern GCP services to support analytics and business intelligence initiatives.
Key Responsibilities
- Design, develop, and optimize scalable data pipelines on GCP.
- Build and maintain batch, event-driven, and real-time data ingestion and processing frameworks.
- Develop robust ETL/ELT pipelines for integrating data from multiple internal and external sources.
- Design and implement efficient data models to support reporting, analytics, and data science use cases.
- Create and manage data orchestration workflows using Cloud Composer (Apache Airflow).
- Develop event-driven architectures using Pub/Sub, Cloud Functions, and other cloud-native services.
- Optimize data storage, transformation, and querying performance using BigQuery.
- Ensure data quality, consistency, governance, and security best practices.
- Collaborate with Data Analysts, Data Scientists, Architects, and business stakeholders to translate requirements into scalable solutions.
- Monitor, troubleshoot, and improve data platform performance and reliability.
Required Skills & Experience
GCP Services (Must Have)
Hands-on experience with one or more of the following GCP services:
- BigQuery
- Cloud Composer (Airflow)
- Pub/Sub
- Cloud Functions
- Cloud Storage
- Dataflow
- Cloud Run (good to have)
Core Technical Skills
- Strong experience in designing and implementing data models (Star Schema, Snowflake Schema, Dimensional Modeling).
- Proven experience in building and maintaining ETL/ELT pipelines.
- Expertise in developing:
- Batch-based data pipelines
- Event-driven data pipelines
- Real-time/streaming data pipelines
- Advanced SQL skills with performance tuning experience.
- Strong Python programming experience for data engineering workflows.
- Experience working with structured and semi-structured data formats (JSON, Parquet, Avro, CSV).
- Knowledge of data warehousing concepts and best practices.
