GCP Data Engineer Jobs Opening in For a Client of TeamLease Digital at Bengaluru
FEATURED JOBGCP Data Engineer
3000000 - 3200000 Monthly 
Job Description
Required Skills & Qualifications:
Job Requirement: GCP Data Engineer - SQL, Python, GCP (BQ, Dataporc, Airflow)
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:
5-7 yearsâ experience in IT or professional services experience in IT delivery or large-scale IT analytics projects
Candidates must have 5+ years of expertise working on 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
