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Data Integration Engineer (Req #1712)

Clover Health

Remote / United States of America
  • Job Type: Full-Time
  • Function: Data Science
  • Industry: Digital Health
  • Post Date: 04/16/2024
  • Website: cloverhealth.com
  • Company Address: 30 Montgomery St, Jersey City, NJ 07306, US

About Clover Health

Clover Health (Nasdaq: CLOV) is a physician enablement company focused on seniors who have historically lacked access to affordable, high-quality healthcare.

Job Description

Salary Range: $132,974 /yr - $161,250 /yr


Job Description: Lead the development and deployment of healthcare EDI and
proprietary interfaces. Work closely with developers, data architects, business analysts,
and business users to implement robust development and monitoring infrastructure for
high volume ETL packages. Closely collaborate with users and trading partners to design,
develop, test, and monitor EDI communications. Create and manage ETL packages,
triggers, stored procedures, views, SQL transactions. Develop new secure data feeds with
external parties as well as internal applications including the data warehouse and business
intelligence applications. Perform analysis and QA. Diagnose ETL and database related
issues, perform root cause analysis, and recommend corrective actions to management.
Work with a small project team to support the design, development, implementation,
monitoring, and maintenance of new ETL programs. Telecommuting permissible from
any location in the U.S.

Requirements: Bachelor’s degree or foreign degree equivalent in Computer Science,
Engineering (any), or related field and five (5) years of progressive, post-baccalaureate
experience in healthcare and health insurance data pipeline development or in the job
offered or related role.

SKILLS: Experience and/or education must include: 
1. Medical claims data;
2. SQL;
3. Python;
4. ETL development;
5. Data analysis including analyzing and debugging query performance, reviewing
database queries and table for correctness and completeness, and producing
analysis of data to identify problems or gaps;
6. Data modeling; and
7. General Database administration.

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