GCP Data Engineer

Cloud Analytics Technologies, LLC; Tampa (FL); On-site; USD 120,000 - 190,000; Full time

Job summary

Cloud Analytics Technologies, LLC is seeking an experienced GCP Data Engineer to design and maintain scalable cloud-based data pipelines on Google Cloud. You will work with large datasets, build ETL/ELT workflows, and optimize data warehouses to support analytics initiatives.

You will implement batch and streaming pipelines using Dataflow, Beam, Spark, and Airflow, manage BigQuery datasets, and ensure data quality and security across the platform.

Qualifications

• Experience with GCP data services and BigQuery.
• Proficient in Python and SQL.
• Experience building ETL/ELT pipelines and data warehouses.
• Familiar with data modeling and data quality validation.
• Knowledge of IAM and security best practices on GCP.

Responsibilities

• Design and implement scalable batch and streaming data pipelines on GCP.
• Build ingestion frameworks to load data from APIs, databases, files, and streaming sources into BigQuery.
• Develop data transformation logic using SQL, Python, Apache Beam, and Spark where appropriate.
• Create and maintain BigQuery datasets, tables, views, and optimized analytical data models.
• Orchestrate and monitor workflows using Cloud Composer and Airflow DAGs.
• Implement real-time processing solutions using Pub/Sub and Dataflow.
• Optimize query performance, storage usage, and cloud infrastructure costs.
• Build data quality checks, reconciliation processes, and pipeline failure recovery mechanisms.
• Integrate data from enterprise systems, cloud applications, and on-premises sources.
• Implement data access controls, governance, lineage, and security policies.
• Configure logging, monitoring, alerting, and troubleshooting for production pipelines.
• Collaborate with data architects, analysts, BI developers, and machine learning teams.
• Maintain technical documentation and support production releases.

Skills

Python

SQL

Data modeling

ETL/ELT

Cloud data pipelines

BI/Analytics

CI/CD

Security best practices

Tools

BigQuery

Cloud Dataflow

Apache Beam

Cloud Composer

Apache Airflow

Pub/Sub

Cloud Storage

Dataproc

Spark

IAM

Git

CI/CD tools

Infrastructure as Code

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

We are looking for an experienced GCP Data Engineer to build and maintain scalable cloud-based data pipelines and data processing solutions on Google Cloud Platform. The candidate will work with large datasets, develop ETL/ELT workflows, optimize data warehouses, and support analytics and business intelligence initiatives.

Technical Skills:

Strong experience with Google Cloud Platform (GCP) data services.

Hands-on experience with BigQuery , including SQL optimization, partitioning, clustering, and cost management.

Strong programming skills in Python and advanced SQL.

Experience developing data pipelines using Cloud Dataflow and Apache Beam .

Experience with Cloud Composer (Apache Airflow) for workflow orchestration.

Knowledge of Pub/Sub for real-time data ingestion and event-driven processing.

Experience with Cloud Storage, Dataproc, and Spark for batch processing.

Understanding of data modeling, dimensional modeling, and data warehouse architecture.

Experience with ETL/ELT development, data transformation, and data quality validation.

Knowledge of IAM, service accounts, encryption, and GCP security best practices.

Familiarity with Git, CI/CD, and infrastructure automation.

Roles and Responsibilities

Design and implement scalable batch and streaming data pipelines on GCP.

Build ingestion frameworks to load data from APIs, databases, files, and streaming sources into BigQuery.

Develop data transformation logic using SQL, Python, Apache Beam, and Spark where appropriate.

Create and maintain BigQuery datasets, tables, views, and optimized analytical data models.

Orchestrate and monitor workflows using Cloud Composer and Airflow DAGs.

Implement real-time processing solutions using Pub/Sub and Dataflow.

Optimize query performance, storage usage, and cloud infrastructure costs.

Build data quality checks, reconciliation processes, and pipeline failure recovery mechanisms.

Integrate data from enterprise systems, cloud applications, and on-premises sources.

Implement data access controls, governance, lineage, and security policies.

Configure logging, monitoring, alerting, and troubleshooting for production pipelines.

Collaborate with data architects, analysts, BI developers, and machine learning teams.

Maintain technical documentation and support production releases.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.

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