Gcp Data Engineer

We are looking for an experienced Data Engineer with deep expertise in Google

Cloud Platform (GCP) to design, build, and manage end-to-end data systems that

drive analytics, business intelligence, and AI initiatives.

In this role, youll be responsible for building high-performance data pipelines that

handle data ingestion, transformation, and integration (ETL/ELT) across multiple

systems. Beyond ETL, you will design the broader data infrastructure, including

data lakes, workflow orchestration, API-driven integrations, and CI/CD automation

to ensure scalability, reliability, and seamless data flow across the enterprise.

This is a hands-on engineering position that blends data architecture, software

engineering, and cloud infrastructure expertise. Youll collaborate closely with

analytics, data science, and product teams to enable a modern, data-driven

ecosystem.

Key Responsibilities

• Design and develop scalable data pipelines using GCP services such as

BigQuery, Dataflow (Apache Beam), Pub/Sub, Dataproc, and Cloud

Composer (Airflow).

• Implement end-to-end ETL/ELT workflows that extract data from multiple

sources, transform it into meaningful formats, and load it into data

warehouses or data lakes.

• Architect and maintain data lake environments using Cloud Storage and

integrate with downstream analytical tools.

• Automate data ingestion frameworks from APIs, streaming platforms, and

external systems into the cloud.

• Implement workflow orchestration and job automation with Airflow/Cloud

Composer to ensure smooth and reliable pipeline execution.

• Enable real-time data streaming and event-driven processing using Pub/Sub,

Kafka, or Dataflow.

• Collaborate with analytics and ML teams to design data models, feature

stores, and data marts that power reporting and AI solutions.

• Develop API-based data services and reusable connectors to enhance

cross-platform data accessibility.

• Integrate CI/CD best practices for data pipelines, including testing,

versioning, and automated deployment through Git, Terraform, and Cloud

Build.

• Establish data governance, lineage, and observability frameworks to ensure

data quality, security, and compliance.

Required Skills & Experience

• 4–7 years of experience in data engineering or ETL development with large-

scale data systems.

• Strong proficiency in Google Cloud Platform (BigQuery, Dataflow, Pub/Sub,

Dataproc, Cloud Storage, Composer).

• Solid understanding of ETL/ELT design principles, data modeling, and data

warehouse architecture.

• Proficiency in Python, SQL, and experience with distributed data processing

frameworks (Apache Beam, Spark).

• Experience building data lakes and managing schema evolution in

structured and semi-structured data (JSON, Parquet, Avro).

• Skilled in workflow orchestration (Airflow, Dagster, or Prefect) and

infrastructure automation (Terraform, Cloud Deployment Manager).

• Familiarity with API integrations, event streaming, and real-time data

processing.

• Google Professional Data Engineer Certification or equivalent.

• Experience with modern data stack tools like DBT, Looker, or Dataform.

• Familiarity with machine learning data pipelines or feature engineering

workflows.