Software Engineer II - Data QA at Abacus Insights

Kathmandu Metropolitan City, Bagamati Province, Nepal -

Full Time

Start Date

Immediate

Expiry Date

08 Jan, 26

Salary

0.0

Posted On

10 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Quality Assurance, Data Engineering, SQL, Python, Data Analysis, Quality Assurance, Analytics Tools, HEDIS Knowledge, Pyspark, Databricks, AWS, ETL Testing, Agile, User Acceptance Testing, Test Management Tools, Jira

Industry

Software Development

Description

Abacus Insights is a mission-driven, growth-stage technology company focused on transforming the healthcare industry, ultimately creating a more personalized patient experience, improving health outcomes, and lowering the overall cost of healthcare. Abacus Insights provides a flexible, efficient, and secure platform that organizes and exchanges healthcare data from various sources and formats. Our platform allows our customers to uncover differentiated insights that address their client's needs. In addition, our employees know they play an active role in keeping our customers' data safe and are responsible for ensuring that our comprehensive policies and practices are met. With our deep expertise in cloud-enabled technologies and knowledge of the healthcare industry, we have built an innovative data integration and management platform that allows healthcare payers access to historically siloed and inaccessible data. Through our platform, these healthcare organizations can ingest and manage all the data they need to transform their business by supporting their analytical, operational, and financial needs. Since our founding in 2017, Abacus has built a highly successful SaaS business, raising $100 Million from leading VC firms with deep expertise in the healthcare and technology industries. We are solving problems of massive scale and complexity in a sector ready for disruption. We're growing quickly and would love for you to be a part of it! About the Role: Our TechOps team is looking to bring on an experienced Data QA Engineer. If you are interested in being a guiding voice on a critical feature delivery team, this position is for you. The connector factory team is responsible for implementing and configuring the data pipelines that ingest and do the ETL and process diverse sources of data. This includes building large batch processing and streaming systems. You will be exposed to every area of our platform and AWS services (Serverless – Lambda, EMR – Hadoop/Spark, EKS – Kubernetes, etc.) but even more so, help drive the evolution of the product and team as we continue to grow rapidly. We believe heavily in architecture evolution, and you will bring your experience to best practices as we build out new components of the platform. You Will: Review and Identify business requirement gaps from mapping specification document Effort estimation of QA activities/tasks Identify mapping test scenarios and authoring ETL test cases Understand the file layout of various input file formats and perform parser validation Write Source and Target SQLs for data validation Automation of Source to target validation in Pyspark, Databricks Identify data needs and perform test execution with Synthetic data that resembles real time data Writing SQLs for User Acceptance Criteria User Acceptance testing (UAT) support Perform day-to-day activities using Agile methodologies Capture QA activities using test management tools and Jira Onsite – offshore coordination on day-to-day activities What We're Looking For: Educational Background: Bachelor’s degree in computer science, Engineering, Information Technology, Information Management, Computer Applications, or a closely related field. Professional Experience: At least 2 years of hands-on experience in Data Quality Assurance or Data Engineering QA roles. Healthcare Domain Expertise: Direct experience working with U.S. healthcare datasets is essential. SQL Proficiency: Expert-level skills in SQL, including writing complex queries, performing data validation, executing joins and aggregations, and optimizing query performance. Python Competency: Advanced Python capabilities for data analysis, synthetic data creation, and automation of data QA processes. Data Analysis Skills: Strong analytical abilities to work with large datasets, detect inconsistencies, and verify business logic. Quality Assurance Practices: Solid foundation in QA methodologies, with proven experience in designing test cases for data-centric systems. Analytics Tools Exposure: Familiarity with analytics platforms and reporting tools is advantageous. HEDIS Knowledge: Understanding of HEDIS quality measures is a plus. Communication Skills: Exceptional verbal and written communication skills, with the ability to clearly present technical insights and findings. Experience with Pyspark, Databricks, AWS services like Redshift, S3, Athena Nice to Have: Experience with other Cloud Data Warehouses (Big Query, Snowflake) Hands on experience of API testing ETL testing automation experience Equal Opportunity Employer As a mission-led technology company helping to drive better healthcare outcomes, Abacus Insights believes that the best innovation and value we can bring to our customers comes from diverse ideas, thoughts, experiences, and perspectives. Therefore, we dedicate resources to building diverse teams and providing equal employment opportunities to all applicants. Abacus prohibits discrimination and harassment regarding race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

Responsibilities

The Data QA Engineer will review business requirement gaps, estimate QA activities, and author ETL test cases. They will also validate data through SQL and automation processes while supporting user acceptance testing.