$117K - $140K/yr
Full-time
Re-posted 7 days ago
Job description
Title: Data Engineer - GCP
Location: Denver, CO (Remote)
Job Summary
The client is seeking a highly skilled Data Engineer with deep expertise in Google Cloud Platform (GCP) and modern data architecture. The ideal candidate will have hands-on experience designing scalable data pipelines, implementing Medallion Architecture, and building robust enterprise-grade data solutions.
This role requires strong technical proficiency in BigQuery, PySpark, Dataflow, and Airflow, along with a solid understanding of cloud data governance, performance optimization, and CI/CD practices.
Key Responsibilities
Required Skills & Experience
Preferred Qualifications
Location: Denver, CO (Remote)
Job Summary
The client is seeking a highly skilled Data Engineer with deep expertise in Google Cloud Platform (GCP) and modern data architecture. The ideal candidate will have hands-on experience designing scalable data pipelines, implementing Medallion Architecture, and building robust enterprise-grade data solutions.
This role requires strong technical proficiency in BigQuery, PySpark, Dataflow, and Airflow, along with a solid understanding of cloud data governance, performance optimization, and CI/CD practices.
Key Responsibilities
- Design, develop, and maintain scalable batch and real-time data pipelines on GCP
- Implement and manage Medallion Architecture (Bronze, Silver, Gold layers) for data processing
- Build high-performance data transformations using Python and PySpark
- Develop and optimize complex SQL queries for analytical workloads
- Work extensively with BigQuery for large-scale data processing and performance tuning
- Develop and deploy pipelines using Cloud Dataflow
- Orchestrate workflows using Cloud Composer (Apache Airflow)
- Manage data storage and lifecycle using Google Cloud Storage (GCS)
- Implement version control and CI/CD pipelines using Git-based tools
- Ensure data security, governance, and access control using GCP IAM
- Optimize data solutions for performance, scalability, reliability, and cost-efficiency
Required Skills & Experience
- Strong hands-on experience with Google Cloud Platform (GCP)
- Expertise in BigQuery (partitioning, clustering, query optimization)
- Proven experience implementing Medallion Data Architecture
- Strong programming skills in Python and PySpark
- Advanced proficiency in SQL (complex joins, window functions, performance tuning)
- Hands-on experience with Cloud Dataflow
- Experience with Cloud Composer (Airflow) for orchestration
- Experience working with Google Cloud Storage (GCS)
- Knowledge of version control systems (Git) and CI/CD practices
- Strong understanding of GCP IAM and cloud security best practices
Preferred Qualifications
- Experience working with large-scale enterprise data platforms
- Knowledge of data warehousing and data lake concepts
- Familiarity with real-time streaming frameworks
- Experience in data governance and data quality frameworks
- Exposure to Agile/Scrum methodologies
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Frequently asked questions
Q: What skills or qualities help someone succeed as a Data Software Engineer?
A: To succeed as a Data Software Engineer, key technical skills include proficiency in programming languages such as Python, Java, or C++, as well as expertise in data structures, algorithms, and software development methodologies like Agile. Additionally, strong soft skills like effective communication, problem-solving, and collaboration are crucial, as Data Software Engineers often work with cross-functional teams and stakeholders to design, develop, and deploy data-driven solutions. By combining technical expertise with strong soft skills, Data Software Engineers can effectively drive business outcomes, innovate, and adapt to the rapidly evolving landscape of data technology.
Q: What is the career path for a Data Software Engineer?
A: A Data Software Engineer's typical career progression involves starting as a Junior Software Engineer, where they focus on developing and maintaining data-driven software applications, and gradually advancing to roles such as Senior Software Engineer, Technical Lead, or Data Architect, where they oversee large-scale data systems and lead cross-functional teams. Key opportunities for skill development include learning programming languages like Python, SQL, and Java, as well as data science tools like Hadoop, Spark, and machine learning frameworks like TensorFlow and PyTorch. Long-term, Data Software Engineers may pursue leadership roles, such as Director of Engineering or Chief Technology Officer, or transition into related fields like data science, product management, or entrepreneurship.