Data Quality Engineer

December 8, 2024

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Loopback Analytics

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Loopback Analytics is a company that connects health systems and life sciences to help understand the patient journey and improve access to care. They offer solutions for health system specialty pharmacies and provide modules designed by pharmacists to facilitate assessment and reporting. Loopback Analytics uses anonymized real-world patient data across the United States to help understand diagnoses, compare drug uses, and engage with therapy-specific outcomes. They focus on various therapeutic areas such as multiple sclerosis, rheumatology, and cystic fibrosis. The company also provides payor access services, aiding in payor contracting and reporting. Loopback Analytics aims to bridge gaps within healthcare by leveraging data insights for improved care delivery and patient outcomes.

Population Analytics • Evidence-Based Models • Post-Acute Care Coordination • Medication Adherence • Community-Based Services

51 - 200 employees

Founded 2009

⚕️ Healthcare Insurance

💊 Pharmaceuticals

🧬 Biotechnology

💰 Private Equity Round on 2023-06

📋 Description

• Enforce and maintain robust data quality standards and protocols. • Monitor and evaluate data quality metrics to identify and address anomalies and inconsistencies. • Participate in efforts to identify and address data-related errors and discrepancies promptly. • Collaborate with technical and non-technical teams to resolve issues and prevent future occurrences. • Develop and implement data quality assurance processes and procedures. • Design and execute data quality checks and validations to ensure accuracy, completeness, and consistency of data. • Collaborate with data engineers and data analysts to understand data pipelines and identify potential sources of data quality issues. • Investigate and report data quality issues in a timely manner. • Develop and maintain data quality metrics and reports to monitor and communicate the health of our data assets. • Develop, document and maintain manual and automation tests for ETL data pipelines using various frameworks like PyTest or Great Expectations. • Work closely with cross-functional teams to define data quality requirements and standards. • Provide technical expertise and guidance on data quality best practices. • Automate data quality monitoring and validation processes using Python and Databricks. • Perform data profiling and analysis to identify patterns and trends in data quality issues. • Stay up-to-date with industry trends and emerging technologies related to data quality management.

🎯 Requirements

• A deep understanding of data management principles, data governance, data integration, data quality, and data architecture. • 3-5 years experience with proficiency in working with relational databases, NoSQL databases, data warehouses, and big data technologies such as Snowflake, Databricks, Spark, etc. • Bachelor's or Master's degree or relevant experience in a field such as Computer Science, Data Science, Healthcare Informatics. • Experience in working with healthcare data and familiarity with the intricacies of healthcare data management. • Proven experience working as a Data Quality Engineer or in a similar role. • Strong proficiency in Python for data manipulation, analysis, and automation. • Hands-on experience with Databricks for data processing and analytics. • Familiarity with Snowflake data warehouse platform is a plus. • Solid understanding of data quality concepts, frameworks, methodologies, commercial tools and best practices. • Experience with data profiling tools and techniques. • Travel expectation less than 15%