Data Architect

Location: Los Angeles, CA
Date Posted: 12-21-2017
Must be able to work for any U.S. employer without restrictions or need for sponsorship

About the Company

Our client leads the health care industry in improving health and reducing claims costs for health plan members with heath care needs that cause or exacerbate co-existing medical conditions. They have an intensive outpatient program that treats an overlooked population. These members cost health plans millions of medical dollars primarily due to preventable health care usage and assistance and medical inpatient admissions.  They use a proprietary analytics platform and predictive modeling to identify the users of medical healthcare services with costs that can be significantly reduced by addressing the underlying issues. Through proactive outreach, they engage and treat these individuals in a patient-centric program that integrates evidence-based medical interventions and care by medical staff.

Why Work Here
  • Ability to work remote
  • Great pay and excellent benefits (healthcare, 401K, holidays, vacation, sick days, etc.)
  • Company culture encourages growth and promotions
  • Employees routinely recognized for their efforts and supported educational opportunities
  • Supportive and knowledgeable co-workers
  • Positive work environment
  • Company that provides help to people who otherwise might not receive proper treatment
  • A company that is rapidly growing, innovative and dedicated to creating a world-class culture
  • Close-knit team and work atmosphere
  • Cutting-edge use of data analytics for innovative care models that achieve program success
  • Leadership shares vision, achievements and goals and welcomes your ideas and feedback
  • Meaningful work environment where your efforts are helping improve other people’s lives
  • Paid travel expenses and paid training

About your Role

As a Data Architect, you’ll own oversight of the production databases. You will be responsible for the data architecture within the organization including MDM/ETL operations, Operational and Business Analytical reporting and Operational efficiencies.  You will work with the application development team and the data science team to support production and research development objectives.  You will be responsible for configuration, up-time, capacity planning, account creation and maintenance, backups/restores, high availability planning and testing. You will work with the DevOps team to create new automation and builds and you’ll work with the data operations team for data quality and availability.

Qualifications of the Role
  • Excellent understanding of Hadoop clusters in a production environment
  • Experience with Hive is preferred
  • Experience with database tuning, access controls (Authentication, Authorization, Audit Logging) and monitoring
  • Knowledge of and experience with Cloudera tools
  • Knowledge of data replication for high-availability and disaster recovery
  • Ability to work collaboratively in a fast-paced, entrepreneurial environment
  • Experienced working with Agile methodologies
  • Passionate about Big Data technologies with a keen interest in integrating statistics and analytics to make systems high-performance
  • Knowledge and experience with cloud technologies

  • Responsible for the configuration and maintenance of all data stores in production, staging and QA environments
  • Maintenance and support of the Hadoop Data Pipeline
  • Proactively monitoring events, investigating issues, analyzing solutions, and driving problems through to resolution using a wide variety of Ops tools
  • Establish and maintain accounts and access controls to all production databases
  • Responsible for data grooming
  • Establishing retention policies and putting them into action
  • Maintaining database monitoring systems and developing new metrics/monitoring dashboards as additional coverage events become necessary
  • Provide insight into the storage and utilization processes within the enterprise
  • Providing development support for data model and query pattern best practices
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