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Use a systematic approach to plan, create, and maintain data architectures while also keeping it aligned with business requirements
Conduct research in the industry to address any issues that can arise while tackling a business problem
Use descriptive data model for data aggregation to extract historical insights
Make predictive models to apply forecasting techniques with actionable insights
Utilize prescriptive models, allowing users to take advantage of recommendations for outcomes
Skills in Demand by companies on Hired:
4+ years of hands-on experience working in data warehousing, data architecture and/or data engineering environments at an enterprise scale
Python, SQL, AWS, JAVA, Spark, ETL, Kafka, Scala, Go Language Kubernees, Hadoop, Data Warehousing, PostgreSQL and Redshift are the most in-demand skills