Google Cloud Data Architect

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Role: Google Cloud Data Architect

Experience: 8+ years

Location: Pan India

Job Summary

Google Cloud Data Architect with strong experience in Designing and Implementing Data Lake, Data Warehouse and Data ingestion and processing pipeline using various Google services. Expertise in building high-performing, scalable, enterprise-grade Data Lake/ Data Warehouse

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.

At least one End-to-end Google Cloud data platform implementation is a must covering all aspects including architecture, design, data engineering, data visualization and data governance (specifically data quality and lineage).

Significant experience with data migrations and development of Operational Data Stores, Enterprise Data Warehouses, Data Lake and Data Marts.

Good hands-on knowledge on Big Query and Data Warehousing life cycle is an absolute requirement.

Significant experience with data migrations and development, design, Operational Data Stores, Enterprise Data Warehouses and Data Marts.

Excellent communication skills to liaise with Business & IT stakeholders.

Expertise in planning execution of a project and efforts estimation.

Understanding of Data Vault, data mesh and data fabric architecture patterns.

Exposure to working in Agile ways of working.

Nice to Have Skills:

Experience in coding languages like Python and PySpark would be an added advantage.

Experience in DevOps, CI/CD, Google Cloud Build , Google Cloud Deploy is a big plus.

IoT/event-driven/microservices in the cloud- Experience with private and public cloud architectures.

Certified: Google Professional Data Engineer

Any Professional/Specialty Google Cloud certification

Cloud native development and deployment in prod

Data Analysis, maintenance, and support experience in cloud infra

Data migration and multi-region replication experience

Regards

Shubhangi