Analytic Software Engineer Iv

This position may be offered to a candidate authorized to work in the US for his/her/their stated employer, without any restrictions which would prevent the candidate from working on the proposed assignment for the duration of the assignment period.

The Analytic Software Engineer IV is a senior technical role in our Business Intelligence
Development team, focus on developing solutions to leverage big data to derive business-ready
insights. The Software Engineer's primary role is to design and implement cloud-based data
analytic solutions for reporting and dashboards. The individual will establish technology
standards and promote software development best practices in the areas of data curation,
analysis, and visualization. The individual will also liaise with business and technical leaders to
deliver scalable, fault-tolerant, effective, and high-quality analytic solutions.

Location: Remote, US

Role qualifications:

• Bachelor's degree (preferably Computer Science, Engineering, or other quantitative fields)
• 7+ years of related experience in designing and implementing production-grade data analytic solutions in Scala, Python, and PySpark.
• 5+ years of experience working with large-scale data and developing SQL queries to extract insights (NA gigs or more).
• 3+ years of hands-on experience with data analytic cloud services, such as AWS EMR, Airflow, and RedShift.
• 3+ years of leveraging statistical techniques to cross-examine multiple data sources to surface patterns and data abnormalities.
• 3+ years of experience working within Business Intelligence tools such as Tableau, Cognos, Qlik, Pyramid.
• Experience in leading data analysts and data engineers in marrying data analytics disciplines with Software Engineering best practices to deliver big data solutions, including modularized pipeline design for big-data solutions, statistically-sound test-driven development, etc.
• Demonstrated knowledge in software development best practices such as Git, linting, unit/integration testing.
• Able to plan work, set clear direction, and coordinate tasks across multi-disciplinary team in a fast-paced environment.
• Collaborative attitude. This role is part of a larger, more dynamic team that nurtures collaboration.
• Excellent communication skills including discussions of technical concepts, conducting peer-programming sessions, and explaining development concepts.
• Flexible and able to embrace change.
• Scala experience is a requirement for this position!

To differentiate yourself, you:

• Experience in healthcare industry data such as X12, CPT/HCPCS, ICD-10.
• Experience in data architecture and cloud engineering.
• Experience working in an "Agile" and/or "Scrum" development environment using tools such as JIRA.
• Experience with operationalization and observability in a production environment.

What you will be doing:

• Design and implement scalable and resilient end-to-end data analytic pipelines.
• Translate complex analytical concepts into modularized software components to surface specific and actionable insights.
• Lead data analysts and data engineers to mature proof-of-concepts and harden them into data analytic solutions.
• Automate manual reporting and analytic processes to improve efficiency and improve user experience using cloud-native technologies.
• Investigate healthcare data, including medical procedures, health conditions, and provider practices.
• Apply business acumen to enhance data quality and curation methodologies to ensure accurate and reliable inputs to analysis and predictive modeling.
• Identify data abnormalities and their root causes and suggest possible steps for mitigation.
• Write and maintain unit and integrations testing suites, QA, and UAT scenarios.
• Contribute to software maintenance and deployment practices, including production code repo.and CI/CD pipeline processes such as git actions and hooks, package and environment creation and maintenance, and updating or implementing infrastructure as code.
• Coach, mentor, and knowledge share for growth of the team and quality improvement. Work with data analysts and data scientists to apply industry standards and optimize analytic jobs.