Responsibilities:
- Works with the Quality Office and cross-functional teams on data quality and implement systems to make them visible with metrics and thresholds
- Responsible for building data pipelines and data integration projects to measure quality
- Build and optimize data queries from multiple relational databases, data warehouses, and data lakes for various analyses and requests
- Support the team to production business requests and analysis, while building tools and infrastructure to improve their speed and effectiveness
- Ability to translate raw data into visualizations to be understood by multiple (technical and non-technical stakeholders) or in data staging.
- Build on the cloud
Core Requirements
- Data engineer with 3-5 years experience in cloud (AWS etc), Python, Spark, SQL, Big-data solutions
- Clear database fundamentals, data integration, transformation, distributed computing, and building robust and scalable solutions.
- Knowledge of API, BI and AI/ML is preferred.
Skills:
- Experience in data integration of multiple databases (RDS, Aurora, SQL data stores - Postgres, Oracle, MySQL; data warehouses - Redshift, Snowflake)
- Strong hands-on experience with data warehousing, ETL, dashboarding, and reporting, involving large datasets and multiple data sources
- Knowledge of Cloud Data Platform/ Services. E.g. AWS S3 /Glue/Lambda/Athena/ EMR/ IAM/ EC2/ Kinesis/ Terraform cloud formation/ Autoscaling etc.
- Experience in distributed computing/scripting (Spark, Python) with a track record of clean, efficient code design.
- Sound understanding of SQL fundamentals and experience with data warehousing, data modeling and schema.
- Dedicated to fast and continual learning - technologies, languages, systems, etc.
- Bachelor's or Master's in Computer Science or equivalent work experience.
- Analytical, problem-solving solving, and demonstrated follow-through.
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