MH: Proven hands-on experience of data architecture and engineering for enterprise grade Datawarehouse/lake/lake-house with overall experience between 7 - 18 years
MH: Demonstrated ability tounderstand technology and architectural strategy & processes& its successful translation into engineering solutions
MH: Deep with BigQuery for data warehousing/lakes/lakehouses and analytics, leveraging Google Clouds suite of tools for data engineering tasks, Parquet format for efficient data storage, open table formats(Iceberg) and building robust data pipelines leveraging cloud native toolsets, ETL processes, data integration, and transformation techniques to ensure seamless, Google Cloud Storage, Google Cloud Pub/Sub, Google Cloud Dataflow, Google Cloud Dataproc, Google Cloud Composer, and Google Cloud Bigtable, looker studio, powerbi etc.
MH: Experience with machine learning models and AI solutions on Google Cloud, utilizing tools like Vertex AI for model deployment and management.
MH: Understanding of data governance, security, and compliance measures to protect sensitive data and ensure adherence to industry standards.
MH: Proficiency in data quality management, including monitoring, measuring, and improving data quality throughout its lifecycle. Familiarity with data governance frameworks to establish rules, processes, and roles that ensure data is managed effectively from initial collection to final analysis
MH: Proficiency in tools like Github (with co-pilot and actions), Build pipelines with relevant technology knowledge, Java, Diverse set of IDEs, Jira, Confluence, Kubernetes, Docker, Google Cloud Platform, Jenkins (Versioning and delivery helper tools), Postman, Websocket client (API development and debug tools).

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