About the Role
As an Associate Data Engineer, you will be instrumental in designing and building new data pipelines for omni-channel brands. Your role will involve ingesting data from various sources such as Salesforce Marketing Cloud, Google Analytics, Firebase, Commerce Cloud, Service Cloud, and other internal and external sources. You will be responsible for maintaining data integrity and quality, with reporting activities conducted on a weekly and monthly basis. Additionally, you will create prototype products, which include Machine Learning (ML) models and basic front-end applications, and support the end-to-end development of these products.
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Responsibilities
- Design, develop, and implement data pipelines to extract, transform, and load (ETL) data from various sources into the data warehouse.
- Optimize data pipelines for performance, scalability, and reliability to handle large volumes of data.
- Work with AWS services such as AWS Glue, AWS Data Pipeline, and Amazon S3 to build robust data processing workflows and API gateways.
- Utilize Tableau to create interactive and insightful data visualizations and dashboards for business stakeholders.
- Troubleshoot data-related issues, identify bottlenecks, and implement effective solutions.
- Exposure to Machine Learning (ML), Natural Language Processing (NLP), and various Large Language Models (LLMs) is considered an advantage.
Qualifications
Education/Certification and Continued Education
- Bachelor's degree in Computer Science, Software Engineering, or Information Technology.
Years of Experience
- Minimum of 4 years of experience with an open mindset and eagerness to learn and experiment with data to create value and expand knowledge.
Knowledge and Skills
- Proficiency in Python and SQL.
- Familiarity with Salesforce Commerce Cloud.
- Experience in streaming and batch processing of data.
- Knowledge of cloud computing platforms, specifically GCP and AWS.
- Experience with visualization tools such as Tableau, Data Studio, or Metabase.
- Experience with CI/CD tools such as Bitbucket, GitHub, or GitLab.
- Basic understanding of Machine Learning concepts like logistic regression, clustering, and NLP.
- Exposure to ML, NLP, and various LLMs is a plus.
