GCP Data Engineer

Responsibilities:

• GCP Solution Architecture & Implementation: Implement and architect data solutions on Google Cloud Platform (GCP), leveraging its various components.
• End-to-End Data Pipeline Development: Design and create end-to-end data pipelines using technologies like Apache Beam, Google Dataflow, or Apache Spark.
• Data Ingestion & Transformation: Implement data pipelines to automate the ingestion, transformation, and augmentation of data sources, providing best practices for pipeline operations.
• Data Technologies Proficiency: Work with Python, Hadoop, Spark, SQL, BigQuery, BigTable, Cloud Storage, Datastore, Spanner, Cloud SQL, and Machine Learning services.
• Database Expertise: Demonstrate expertise in at least two of these technologies: Relational Databases, Analytical Databases, or NoSQL databases.
• SQL Development & Data Mining: Possess expert knowledge in SQL development and experience in data mining (SQL, ETL, data warehouse, etc.) using complex datasets in a business environment.
• Data Integration & Preparation: Build data integration and preparation tools using cloud technologies (like Snaplogic, Google Dataflow, Cloud Dataprep, Python, etc.).
• Data Quality & Regulatory Compliance: Identify downstream implications of data loads/migration, considering aspects like data quality and regulatory compliance.
• Scalable Data Solutions: Develop scalable data solutions that simplify user access to massive data, capable of adapting to a rapidly changing business environment.
• Programming: Proficient in programming languages such as Java and Python.

Required Skills:

• GCP Data Engineering Expertise: Strong experience with GCP Data Engineering, including BigQuery, SQL, Cloud Composer/Python, Cloud Functions, Dataproc + PySpark, Python injection, Dataflow + Pub/Sub.
• Expert knowledge of Google Cloud Platform; other cloud platforms are a plus.
• Expert knowledge in SQL development.
• Expertise in building data integration and preparation tools using cloud technologies (like Snaplogic, Google Dataflow, Cloud Dataprep, Python, etc.).
• Proficiency with Apache Beam/Google Dataflow/Apache Spark in creating end-to-end data pipelines.
• Experience in some of the following: Python, Hadoop, Spark, SQL, BigQuery, BigTable, Cloud Storage, Datastore, Spanner, Cloud SQL, Machine Learning.
• Proficiency in programming in Java, Python, etc.
• Expertise in at least two of these technologies: Relational Databases, Analytical Databases, NoSQL databases.
• Strong analytical and problem-solving skills.
• Capability to work in a rapidly changing business environment.

Certifications (Major Advantage):

• Certified in Google Professional Data Engineer/Solution Architect.