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Key job responsibilities2. Actively participates in the code review process, design discussions, team planning, operational excellence, and constructively identifies problems and proposes solutions
3. Makes appropriate trade-offs, re-use where possible, and is judicious about introducing dependencies
4. Makes efficient use of resources (e.g., system hardware, data storage, query optimization, AWS infrastructure etc.)
5. Asks correct questions when data model and requirements are not well defined and comes up with designs which are scalable, maintainable and efficient
6. Makes enhancements that improve team’s data architecture, making it better and easier to maintain (e.g., data auditing solutions, automating, ad-hoc or manual operation steps)
7. Owns the data quality of important datasets and any new changes/enhancements
- 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)
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience writing and optimizing SQL queries with large-scale, complex datasets
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
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