Job description

Opportunity Description

Our client is looking for a Data Engineer to join our digital data team in the data
architecture operation and governance team to build and operationalize data
pipelines necessary for the enterprise data and analytics and insights initiatives,
following industry standard practices and tools. The bulk of the work would be in
building, managing, and optimizing data pipelines and then moving them effectively into production for key data and analytics consumers like business/data analysts, data scientists or any persona that needs curated data for data and analytics use cases across the enterprise. In addition, guarantee compliance with data governance and data security requirements while creating, improving, and
operationalizing these integrated and reusable data pipelines.

The data engineer will be the key interface in operationalizing data and analytics on behalf of the business unit(s) and organizational outcomes.

Tech skills

  • Knowledge of AWS.
  • Knowledge of Azure or GCP is a plus
  • Orchestration: Airflow
  • Project management & support: JIRA projects & service desk, Confluence,
  • Expert in ELT and ETL such as Informatica IICS, Databricks, Delta, Glue, ...
  • Expert in Relational database technologies and concepts:
  • Perform SQL queries
  • Create database models
  • Maintain and improve queries performance
  • Snowflake is a plus
  • Working knowledge of Python and familiar with other scripting languages
  • Good knowledge of cloud computing

Soft Skills

  • Pragmatic and capable of solving complex issues
  • Ability to understand business needs
  • Good communication
  • Push innovative solutions
  • Service-oriented, flexible & team player
  • Self-motivated, take initiative
  • Attention to detail & technical intuition


  • At least 5 years experiences in a data team as Data Engineer
  • Experience in a healthcare industry is a strong plus

Preferred Qualifications

  • BS or MS in Computer Science



  • Must work with business team to understand requirements, and translate
    them into technical needs
  • Gather and organize large and complex data assets, perform relevant
  • Ensure the quality of the data in coordination with Data Analysts and Data
    Scientists (peer validation)
  • Propose and implement relevant data models for each business cases
  • Optimize data models and workflows
  • Communicate results and findings in a structured way
  • Partner with Product Owner and Data Analysts to prioritize the pipeline
    implementation plan
  • Partner with Data Analysts and Data scientists to design pipelines relevant for business requirements
  • Leverage existing or create new “standard pipelines” within to bring
    value through business use cases
  • Ensure best practices in data manipulation are enforced end-to-end
  • Actively contribute to Data governance community

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