Senior Specialist, Data

Job Description

  • Understand business processes, applications, and how data flows from creation through storage and collection.
  • Design, build, optimize and maintain scalable and reliable data pipelines and ELT/ETL processes on AWS cloud platform.
  • Design, develop and maintain data models, data marts, and analytical datasets to satisfy growing data needs.
  • Establish and own the data quality and availability for the various data flows.
  • Create compelling story and visualisation to convey business insights. Deliver analytics solution to the business.
  • Perform exploratory data analysis (EDA) to support predictive use cases.
  • Leverage existing predictive models (e.g., regression, classification, basic forecasting) on cloud-based ML platforms, performing configuration, parameter tuning and evaluation to meet business requirements.
  • Be part of a project team to manage and deliver project deliverables, resources and timelines.

Requirements

  • Bachelor’s or equivalent degree in Data Science & Analytics, Computer Science or related field
  • At least 5 years of experience in data engineering, data science, or related domains
  • Hands-on experience with SQL, Python, and distributed data systems is required
  • Experience in ELT/ETL pipeline design, implementation and maintenance
  • Experience working with visualization tools (AWS Quick would be preferred)
  • Strong understanding in data architecture, management & governance concepts related to lake-house, data lineage and data quality
  • Good understanding and experience with cloud services, such as AWS
  • Knowledge of cloud-based ML platforms (AWS SageMaker, Azure ML, GCP AI Platform) on data preprocessing, feature engineering, and model validation is a plus
  • Good communication & stakeholder management skills with ability to bridge technical and business requirements
  • Ability to work independently and manage multiple task assignments with motivation and commitment
  • Willingness to stay updated on emerging tools, techniques, and best practices in data engineering and analytics.

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