Vacancy description
Costco IT
India, Hyderabad
Data Engineer - ML & AI in Hyderabad, India is listed on Jobeax. Browse 30,000+ vacancies available.
About Costco Wholesale: Costco Wholesale is a multi-billion-dollar global retailer with warehouse club operations in eleven countries. They provide a wide selection of quality merchandise, plus the convenience of specialty departments and exclusive member services, all designed to make shopping a pleasurable experience for their members.
At Costco Wholesale India, we foster a collaborative space, working to support Costco Wholesale in developing innovative solutions that improve members’ experiences and make employees’ jobs easier. Our employees play a key role in driving and delivering innovation to establish IT as a core competitive advantage for Costco Wholesale.
Data Engineer
The Data Engineer is responsible for developing data pipelines and/or data integrations of for Costco’s enterprise certified data sets that are used for business critical data consumption use cases (i.e. Reporting, Data Science/Machine Learning, Data APIs, etc.). The Data Engineer will partner with product owners, data architects, and data platform teams to design, build, test, and automate data pipelines that are relied upon across the company as the single source of truth.
Develops and operationalizes data pipelines to bring data into Costco’s GCP landscape for the delivery of certified data sets
Works in tandem with Data Architects, Data Stewards, and Data Quality Engineers to design data pipelines and recommends ongoing optimization of data storage, data ingestion, data quality and orchestration.
Designs, develops, and implements ETL/ELT/CDC processes using Data Build Tool (DBT) and other native GCP Services (BigQuery Subscriptions, Dataproc, Dataflow, etc.)
Uses GCP services such as BigQuery, AlloyDB, Spanner, Dataplex, Pub/Sub, Cloud Storage, etc. to improve and speed delivery of our data products and services.
Identifies, designs, and implements internal process improvements: automating manual processes, optimizing pipeline delivery and support.
Identifies ways to improve data reliability, efficiency, and quality of data management
Participate in off hours 24/7 on call support on a rotational basis
5-8+ years’ experience engineering and operationalizing data pipelines with large and complex datasets.
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