Data Analyst/ETL Developer (remote)

Claritev•New York, NY

6h•Remote

About The Position

The Data Analyst/ETL Developer plays a crucial role in providing insights for decision making. The Data Analyst transforms data into usable metrics through data processing software and business development strategies to provide valuable insights to company stakeholders.

Requirements

Bachelor's degree in Computer Science, Data Analytics, Information Technology, Finance, or a related field.
6 months to 1 year of experience with SQL and data analytics
Proficiency in SQL for data manipulation, extraction, and analysis.
Strong analytical and problem-solving skills with the ability to interpret complex data sets.
A proactive problem solver with a keen attention to detail.
Familiarity with agile development methodologies and a collaborative mindset.
Excellent communication skills to effectively translate technical concepts to non-technical stakeholders

Responsibilities

Utilize SQL, and/or Python in data analytics to collect, understand, transform, cleanse, store, and share data in a privacy-preserving way.
Debug mappings and identify errors and error rows so that they can be corrected and re-loaded into a target system.
Review and analyze functional requirements and mapping documents.
Develop, maintain, and support ETL data pipelines for healthcare products and analytics platforms.
Initialize and configure data sites/environments to enable downstream analytics and reporting by partner teams.
Collaborate with upstream and downstream teams to ensure successful site onboarding, handoff, and readiness.
Monitor ETL jobs and pipelines in production, identifying failures, performance degradation, and data anomalies.
Troubleshoot and resolve production issues in ETL workflows, including failed loads, data mismatches, and processing delays.
Perform root cause analysis for pipeline incidents and implement corrective and preventive fixes.
Validate incoming healthcare datasets prior to release for downstream consumption.
Build and maintain data validations to ensure: source to target reconciliation, row counts and completeness, business rule enforcement, and data type, format, and constraint integrity.