Google Cloud Platform Data Engineer -GCP,BigQuery,SQL, Cloud Function

Job Description :

Job Title : Senior GCP Data Engineer

Skills required : GCP, Bigquery, DataProc, Dataflow, GCS, Dataproc, Airflow

Job Requirement :

- Have Implemented and Architected solutions on Google Cloud Platform using the components of GCP

- Experience with Apache Beam/Google Dataflow/Apache Spark in creating end to end data pipelines.

- Experience in some of the following : Python, Hadoop, Spark, SQL, Big Query, Big Table Cloud Storage, Datastore, Spanner, Cloud SQL, Machine Learning.

- Experience programming in Java, Python, etc.

- Expertise in at least two of these technologies : Relational Databases, Analytical Databases, NoSQL databases.

- Certified in Google Professional Data Engineer/ Solution Architect is a major Advantage.

Skills Required :

- Experience in IT or professional services experience in IT delivery or large-scale IT analytics projects

- Candidates must have expertise knowledge of Google Cloud Platform; the other cloud platforms are nice to have.

- Expert knowledge in SQL development.

- Expertise in building data integration and preparation tools using cloud technologies (like Snaplogic, Google Dataflow, Cloud Dataprep, Python, etc).

- Experience with Apache Beam/Google Dataflow/Apache Spark in creating end to end data pipelines.

- Experience in some of the following : Python, Hadoop, Spark, SQL, Big Query, Big Table Cloud Storage, Datastore, Spanner, Cloud SQL, Machine Learning.

- Experience programming in Java, Python, etc.

- Identify downstream implications of data loads/migration (e.g., data quality, regulatory, etc.)

- Implement data pipelines to automate the ingestion, transformation, and augmentation of data sources, and provide best practices for pipeline operations.

- Capability to work in a rapidly changing business environment and to enable simplified user access to massive data by building scalable data solutions

- Advanced SQL writing and experience in data mining (SQL, ETL, data warehouse, etc.) and using databases in a business environment with complex datasets