Are you passionate about working with huge data sets and bringing datasets together to answer business questions of driving growth?
As a Sr. Specialist Analytics, you will champion key technical issues of customers' business problems with cloud architectures. You will be responsible for designing and implementing the complex Extract Transform Load (ETL) pipelines in big data platform and other Business Intelligence (BI) solutions to support the rapidly growing and dynamic business demand for data.
The ideal candidate will be ...
• Trusted advisor to customers: have excellent business and interpersonal skills to work with senior executives, business owners, system architects, and data engineers to understand business requirements. Materialize an overall proposal and efficiently communicate the benefits of the recommended solution.
• Hands-on architect: help customers to build stable, scalable, and low cost solutions to ingest large datasets into the data lake. The datasets are distributed in different data platforms.
• Technology evangelist: enable customers, partners, internal teams about AWS analytics services. Create scalable mechanisms supporting awareness, adoption, and growth such as AWS blogs, white papers, sample codes, workshops, and etc.
• 5+ years of extract transform load (ETL) work experiences of building near-realtime streaming pipelines, change data capture (CDC) system, data tiering (ie. raw, curated, analytics, digested), multi-temperature data(ie. hot, warm, cool), data modeling (ie. star/snowflake schema), and data architectures (ie. lambda)
• 3+ years of relevant work experiences of big data technologies (ie. Apache Spark/Kafka/Hive/Flink/Hudi/Pestro, Elastic Search, Kibana, etc)
• Expert-level skills in writing and optimizing SQL.
• Proficiency in one of programming languages - PySpark, Scala, Python, and Java
• Sound knowledge of data architectures (lambda): knows how to optimize the distribution, partitioning, and massively parallel processing (MPP) of data.
• Demonstrate efficiency in handling data - tracking data lineage, ensuring data quality, and improving discoverability of data.
• Experience operating very large data warehouses and data lakes
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