First things first, you know SQL and data modelling like the back of your hand. You also need to know Big Data and MPP systems. You have a history of coming up with innovative solutions to complex technical problems. You are a quick and willing learner of new technologies and have examples to prove your aptitude. You are not tool-centric; you determine what technology works best for the problem at hand and apply it accordingly. You can explain complex concepts to your non-technical customers in simple terms.
Key job responsibilities
- Work with SDE teams and business stakeholders to understand data requirements and design data ingress flow for team
- Lead the design, model, and implementation of large, evolving, structured, semi-structured and unstructured datasets
- Evaluate and implement efficient distributed storage and query techniques
- Interact and integrate with internal and external teams and systems to extract, transform, and load data from a wide variety of sources
- Implement robust and maintainable code with clear and maintained documentation
- Implement test automation on code implemented through unit testing and integration testing
- Work in a tech stack which is a mix of NAWS services and legacy ETL tools within Amazon
About the team
Data Insights, Metrics & Reporting team (DIMR) is the central data engineering team in Amazon Warehousing & Distribution org which is responsible for 4 things mainly -
- Building and maintaining data engineering and reporting infrastructure using NAWS to support internal/external data use-cases.
- Building data ingestions pipelines from any kind of upstream data sources which include (but not limited to) real time event streaming services, data lakes, manual file uploads, etc.
- Building mechanisms to vend data to internal team members or external sellers with right data handling techniques in place.
- Build robust data mart to support diverse use-cases powered by GenAI tool.
Basic qualifications
- 1+ years of data engineering experience- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)
Preferred qualifications
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
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