Google Cloud Platform Data Engineer/ Architect

Google Cloud Platform Data Engineer

Remote

Contract

Only W2/1099

Detailed JD:

Designs, builds, and maintains scalable data pipelines and architectures on Google Cloud, transforming raw data into accessible insights using tools like BigQuery, Dataflow, and Cloud Storage, ensuring quality, security, and performance, while collaborating with analysts and scientists to meet business needs.

Key responsibilities include ETL/ELT, data warehousing, monitoring, and optimizing cloud data solutions, requiring skills in SQL, Python, and Google Cloud Platform services, along with strong analytical and communication abilities.

Responsibilities

Pipeline Development: Design, build, and maintain robust, scalable data pipelines (batch/streaming) using Google Cloud Platform services.

Architecture: Develop and manage data warehousing and data lake solutions (e.g., BigQuery, Cloud Storage).

Data Quality & Governance: Ensure data integrity, consistency, security, and compliance.

Collaboration: Work with data scientists, analysts, and business stakeholders to define requirements and deliver solutions.

Optimization: Monitor and optimize data infrastructure for performance, cost, and reliability.

Automation: Implement CI/CD, Infrastructure as Code (IaC), and automation for data workflows.

Core Skills & Qualifications

At least 3+ years of experience with Google Cloud Platform data services (BigQuery, Dataflow, Dataproc, Cloud Storage, etc.).

Must have delivered al least 2 to 3 end to end projects as a data engineer using Google Cloud Platform Services

Strong understanding of database design, data modeling (relational, dimensional, NoSQL).

Expertise in data integration, ETL/ELT, and data pipeline development.

Knowledge of cloud security best practices, identity management, and networking.

Familiarity with DevOps, CI/CD, and containerization (Docker, Kubernetes).

Excellent communication, problem-solving, and leadership skills

Google Cloud Platform Data Architect

Remote

Contract

Only W2/1099

Detailed JD:

Designs, builds, and manages scalable, secure data solutions on Google Cloud, focusing on big data, warehousing (like BigQuery), pipelines (ETL/ELT), and governance, collaborating with stakeholders to translate business needs into robust data strategies, ensuring performance, cost-efficiency, and compliance, using Google Cloud Platform tools and cloud-native tech. Key duties involve architecture design, security implementation, cost optimization, and mentoring teams.

Core Skills & Qualifications

At least 8+ years of experience with Google Cloud Platform data services (BigQuery, Dataflow, Dataproc, Cloud Storage, etc.).

Must have delivered 4 to 5 end to end projects as a data architect using Google Cloud Platform Services

Strong understanding of database design, data modeling (relational, dimensional, NoSQL).

Expertise in data integration, ETL/ELT, and data pipeline development.

Knowledge of cloud security best practices, identity management, and networking.

Familiarity with DevOps, CI/CD, and containerization (Docker, Kubernetes).

Excellent communication, problem-solving, and leadership skills.

Key Responsibilities

Architecture & Design: Architecting end-to-end data solutions (data lakes, warehouses, pipelines) using Google Cloud Platform services (BigQuery, Dataflow, Pub/Sub, etc.).

Data Modeling & Integration: Designing data models, schemas, and ETL/ELT processes for data ingestion, transformation, and delivery.

Security & Governance: Implementing security frameworks, access controls, and data governance policies (GDPR, HIPAA, CCPA).

Performance & Cost: Optimizing cloud infrastructure for performance, scalability, and cost-effectiveness.

Collaboration & Leadership: Working with data engineers, analysts, developers, and business stakeholders to define requirements and provide technical guidance.

Documentation: Creating detailed architecture diagrams, design documents, and technical specifications.

Emerging Tech: Evaluating and adopting new Google Cloud Platform features and cloud technologies (AI/ML, IoT, serverless)