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# GCP Data Engineer - Remote / Telecommute

- **Company:** CYNET SYSTEMS INC.
- **Location:** Brentwood, TN, United States (Remote available)
- **Experience:** Expert
- **Salary:** $104,000.0 - $114,400.0
- **Contract:** Temporary to permanent
- **Skills:** Airflow, Big Data, BigQuery, Cloud Storage, Databases, Information Engineering, Extract Transform Load (ETL), Data Security, Data Warehousing, Data Flow Control, Identity and Access Management, Python (Programming Language), Microsoft SQL Server, Performance Tuning, Scrum Methodology, SQL Stored Procedures, SQL Databases, Data Processing, Freeform SQL, Google Cloud, Data Ingestion, Git, Google Bigquery, Software Version Control, Data Pipelines, Apache Beam
- **Published:** August 3, 2026
- **Apply:** https://www.careerjet.com/jobad/us7bd826fcefcf6ef55f116f76f50a1591

## About the Role

* 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 GCP 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.

## Description

* Design, develop, and maintain scalable ETL/ELT pipelines using GCP 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 GCP.
 * 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.


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