Guidepoint
healthcare
SoftwareEngineerIV
Neural analysis suggests this role is
optimal for Senior candidates.
“Software Engineer IV at Guidepoint. Skills: Python, Data platform, ETL, Architecture. Build data applications. Automate business workflows”
What You'll Achieve.
Generate actionable insights; Deliver superior insights; Ship production systems; Improve legacy workflows; Set practical engineering patterns; Help team move faster; Raise quality of Python development
Industry & Context.
Sound technical judgment; Operate independently on complex problems; Translate ambiguous business needs into durable technical solutions
What They're Looking For.
Must Have
Minimum 4 - 7 years of professional software development experience, hands-on expertise in building and maintaining Python applications in critical production environments, Proven experience with data-driven applications, ETL workflows, data pipelines, or platform integrations, Deep understanding of Python development best practices, including application structure, testing, packaging, error handling, observability, and maintainability, proficiency with SQL and relational data modeling concepts, Experience working in Agile software development environments, ownership mindset, sound technical judgment, and ability to operate independently on complex problems, Ability to mentor engineers, influence stakeholders, and communicate technical tradeoffs clearly
Nice to Have
Experience with Snowflake or comparable modern cloud data warehouse platforms, Familiarity with Airflow or other workflow orchestration tools, Knowledge of modern data architecture patterns, data quality frameworks, and data platform engineering practices, Experience using AI-assisted development workflows
What You'll Do.
Build data applications
Automate business workflows
Ensure workflows are observable
Modernize legacy ETLs
Establish data quality patterns
Improve testing practices
Reduce technical debt
Define architecture standards
Partner with analysts
Translate business needs
How You'll Work.
Team & Collaboration
Partner with analysts; Partner with data scientists; Partner with data engineers; Influence stakeholders
Communication Scope
Communicate technical tradeoffs clearly
Full Job Description
Overview Qsight is a high-growth division of Guidepoint focused on building data intelligence solutions for the healthcare sector. Qsight leverages proprietary datasets and rigorous analysis of alternative data sources to generate actionable insights for top-tier institutional investors, medical device manufacturers, and pharmaceutical companies. The Qsight team develops market intelligence products designed to be highly relevant, accurate, and scalable – delivering superior insights to a diverse, global client base. We’re looking for a hands-on Software Engineer IV to own and evolve the Python-based workflows and applications that power our data platform. You’ll be close to the code, close to the business, and influential in how we build: shipping production systems, improving legacy workflows, setting practical engineering patterns, and helping the team move faster with greater confidence. The right person combines deep Python expertise, strong architectural judgment, and a bias for solving real problems with reliable, maintainable software. This is a hybrid position out of our Pune office, or can be fully remote. The ideal candidate will be able to overlap with the US Eastern Time Zone until 12:30 PM (10:00 PM IST) What You'll Do: Build and maintain data applications Build production-grade Python applications and integrations that automate critical business workflows. Own and improve Python-based ETL frameworks, Airflow DAGs, and orchestration patterns. Integrate internal and external systems, including Snowflake, DynamoDB, CRM platforms, ecommerce platforms, and third-party APIs. Ensure critical workflows are observable, reliable, testable, and maintainable. Modernize the Platform Modernize legacy ETLs using pragmatic refactoring, reusable libraries, and AI-assisted development where appropriate. Establish data quality patterns and frameworks, including tools such as Great Expectations. Improve testing, deployment, and operational practices for Python-based data
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