GCP Services

6 days ago

Bengaluru Hyderabad Pune, India Tata Consultancy Services Full time ₹ 5,00,000 - ₹ 15,00,000 per year

Location : Bangalore, Chennai, Hyderabad, Pune, Kolkata

6+ Years Experience in the Google Cloud on Big Data/ Analytics/ Data Lake / Datawarehouse DataProc , DataPrep, Data Plex , Cloud Bigtable , Dataflow , Cloud Composer , BigQuery, Databricks, Kafka, Nifi, CDC processing, Snowflake, Datastore , Firestore, Docker, App Engine , Spark, Cloud Data Fusion, Apigee API Management, Kafka, Attunity, Golden Gate, Map Reduce, Hadoop, Hive, HBase, Cassandra, PySpark, Flume, Hive, Impala

Must Have :

  • Design & Implement ETL/data pipeline and Data Lake/ Datawarehouse in Google Cloud
  • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Big Query, DataProc, Data Prep , Cloud Composer , Dataflow services/technologies.
  • 10+ years of total experience and at least 3+ years of expertise in Cloud data warehouse technologies on Google Cloud data platform covering Big Query, DataProc, Data Prep, Cloud Composer , Dataflow, Databricks etc.
  • Extensive hands-on experience implementing data ingestion and data processing using Google Cloud services: DataProc , Data Prep, Cloud Bigtable , Dataflow , Cloud Composer , Big Query, Databricks, Kafka, Nifi, CDC processing, Snowflake, Datastore , Firestore, Docker, App Engine , Spark, Cloud Data Fusion, Apigee API Management,  etc.
  • Familiarity with the Technology stack available in the industry for data management, data ingestion, capture, processing, and curation: Kafka, Attunity, Golden Gate , Data Plex , Map Reduce, Hadoop, Hive, HBase, Cassandra, PySpark, Flume, Hive, Impala, etc.
  • Design and Implement analytics solution that utilize the ETL/data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
  • Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
  • Create data tools for analytics and data scientist team members that assist them in building and optimizing products.

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