Google Cloud Platform Data Engineer

Google Cloud Platform Data Engineer

Job Location : Remote
Experience
4–8 Years
Job Summary
We are seeking an experienced Google Cloud Platform Data Engineer with strong expertise in MS SQL Server, Python, Google BigQuery, and Google Cloud Dataflow to design, develop, and maintain scalable cloud-based data solutions. The ideal candidate will be responsible for building data pipelines, optimizing data processing workflows, and supporting enterprise analytics and reporting requirements.
Key Responsibilities
 
Design, develop, and maintain scalable ETL/ELT pipelines using Google Cloud Platform Dataflow and Python.
Develop and optimize BigQuery datasets, tables, views, partitioning, and clustering strategies.
Create and maintain complex SQL queries, stored procedures, functions, and database objects in MS SQL Server.
Migrate and integrate data from on-premises databases and various source systems into Google Cloud Platform.
Implement data quality, validation, reconciliation, and monitoring frameworks.
Work with Cloud Storage (GCS), BigQuery, Dataflow, and Composer/Airflow for end-to-end data processing.
Analyze performance bottlenecks and optimize large-scale data workloads.
Build reusable Python components for data ingestion, transformation, and automation.
Collaborate with business stakeholders, architects, and data analysts to understand requirements.
Ensure data security, governance, and compliance standards are followed.
 
Required Skills
 
Strong experience in MS SQL Server (SQL, Stored Procedures, Views, Functions, Performance Tuning).
Proficiency in Python for data engineering and automation.
Hands-on experience with Google BigQuery.
Experience developing data pipelines using Google Cloud Dataflow (Apache Beam).
Knowledge of Google Cloud Platform services such as Cloud Storage, Pub/Sub, Cloud Composer, IAM, and Monitoring.
Strong understanding of ETL/ELT, Data Warehousing, and Data Modeling concepts.
Experience with source control tools such as Git.
Knowledge of Agile/Scrum methodologies