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As a Data Engineer on our team, you'll work with cutting-edge technologies and complex data environments. You'll be responsible for enhancing our existing data architecture to further standardize metrics and definitions, developing end-to-end data engineering solutions for complex analytical problems, and collaborating with analysts, data scientists, and software engineers to translate data into actionable insights.This role requires you to live at the intersection of data, software, and science. We leverage a comprehensive suite of AWS technologies, with key tools including S3, Redshift, DynamoDB, Lambda, API's, Glue, MWAA, DataZone, and SageMaker. You will drive the development process from design to release.Key job responsibilitiesManaging data ingestion from heterogeneous data sources, with automated data quality checks.
Creating scalable data models for effective data processing, storage, retrieval, and archiving.
Using scripting for automation and tool development, which is scalable, reusable, and maintainable.
Providing infrastructure for compensation analytics and science use cases.
Using industry best practices in building CI/CD pipelines.
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Knowledge of software engineering best practices across the development life cycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
- Experience with one or more scripting language (e.g., Python, KornShell, Scala)
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- 1+ years of data engineering experience
- Knowledge of cloud services such as AWS or equivalent
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience in creating, onboarding, and working with APIs and productionizing ML/science algorithms
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