GCP Data Engineer

Key Responsibilities

Solution Architecture: Evaluate data ingestion methods, data transformation pipelines, and data visualization techniques to optimize data analysis workflows. Create data models and schema designs that facilitate efficient querying and data retrieval.Ensure data security and compliance with relevant regulations by implementing appropriate access controls and encryption strategies.

Assist in the review and enhancements of the data architectures leveraging GCP services like Big. Query, Dataflow, Cloud Storage, Cloud SQL, and Cloud Functions to meet complex data processing needs. Data Pipelines & Orchestration: dbt (ata Build Tool): Proficiency in writing dbt models (sources, models, tests, snapshots), managing dbt projects, and utilizing dbt's features (version control, documentation, testing).

Data Pipelines: Building and maintaining robust data pipelines using tools like Apache Airflow, Prefect, or similar. ETL/ELT Processes: Understanding and implementing data extraction, transformation, and loading processes. Data Quality & Testing: Data Validation: Implementing data quality checks and validations within dbt models (e.

g., data type checks, uniqueness constraints, null checks). Testing Frameworks: Utilizing dbt's testing framework or other testing tools to ensure data accuracy and integrity. Cloud Integration: Integrate data from diverse sources (on-premise, cloud-based applications, APIs) into Big. Query using data ingestion tools and techniques.

Develop data integration workflows to ensure data consistency and quality across different systems. Leverage other GCP services like Cloud Dataflow for real-time data processing and stream analytics. Collaboration and Communication: Work closely with data analysts, business stakeholders, and software engineers to understand data requirements and translate them into actionable insights.

Document technical design decisions and provide clear explanations of complex data concepts. Proactively identify potential issues and propose solutions to ensure data quality and system reliability. Technical Expertise: Deep understanding of data warehousing principles, data modeling, and dimensional design. Expertise in SQL and proficiency in other programming languages like Python for data manipulation, automation, and interacting with APIs.

Familiarity with data visualization tools to present insights effectively. Knowledge of cloud computing concepts and best practices on Google Cloud Platform.

Required Skills

Proven experience in designing and implementing data solutions using Google Big. Query and other GCP services. Strong SQL skills with expertise in complex query optimization. Experience with data ingestion and transformation techniques. Excellent problem-solving and analytical abilities. Ability to work independently and as part of a team.

Google Cloud Platform certifications (e.g., Certified Professional Data Engineer, Certified Professional Cloud Architect) are highly desirable.