Design, implement, and support data warehouse / data lake infrastructure using AWS big data stack, Python, Redshift, Quicksight, Glue/lake formation, EMR/Spark/Scala, Athena etc.
• Extract huge volumes of structured and unstructured data from various sources (Relational /Non-relational/No-SQL database) and message streams and construct complex analyses.
• Develop and manage ETLs to source data from various systems and create unified data model for analytics and reporting
• Perform detailed source-system analysis, source-to-target data analysis, and transformation analysis
• Participate in the full development cycle for ETL: design, implementation, validation, documentation, and maintenance.- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- 4+ years of SQL experience
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience as a data engineer or related specialty (e.g., software engineer, business intelligence engineer, data scientist) with a track record of manipulating, processing, and extracting value from large datasets- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
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