Job Description:

Why This Role is Important to Us
The Data Engineer role at Commonwealth Care Alliance (CCA) is a heavy technical and architect role that designs, contributes to, and maintains data pipelines and systems that support clinical point of care and business decisions. This role is highly collaborative with other data-focused departments at CCA including Business Intelligence, Actuarial, and IT departments. This role is a "forward-deployed" engineer that is expected to engage with and continually build a greater understanding of the clinical and operational context. The Data Engineer must understand existing data, infrastructure, and transactional systems to design the appropriate usage of existing data assets or the development of new data assets, including large structured and unstructured data. This role must automate robust workflows to efficiently perform extract, load, transform, modeling, and computational tasks in continuous integration and continuous delivery pattern. The Data Engineer will also support and contribute to rapid prototyping and deployment of analytics, model training, and model deployment. This role also works with stakeholders to optimize current data system and machine learning platform to meet the evolving business needs. This role will work closely with our data scientists, analysts and product managers.

What We're Looking For
Required:

Master's degree

OR bachelor's degree + 5 years of equivalent work experience

Preferred:
(STEM major or related field preferred)

Required:

Technical skills:

• Python, SQL


• BI tool (Looker, Tableau, PowerBI ...)


• Object oriented programming languages (Java, C++ ...)


• Linux and bash scripting


• Git-based version control systems

Soft skills:

• Be organized and flexible


• Take initiative to own projects


• Strong analytical skills


• Strong verbal and written communicational skills


• Be able to work with interdisciplinary teams


• Be able to explain technical concepts/results to non-technical audiences


• Passion for creating work that is well-documented and reproducible


• Ability to work with short iteration times in agile mode as well as the ability to carry out projects in a self-directed manner


• Healthcare: passion for working at a healthcare organization

Preferred:

• Experience with R


• Experience in healthcare or health insurance organization


• Experience with clinical claims and health record data


• Experience building data pipelines in the cloud under the constraints of HIPAA


• Publication or presentation of innovation in data science, machine learning or related area

Machine learning engineer track:

• Fluency in Python


• Shell scripting


• Experience with Docker, Kubernetes


• Experience building pipelines using Python, MLFlow, Kubeflow or other similar cloud native services


• Experience working in Linux environment

Required:
English

What You'll Be Doing

• Build scalable and robust data model/infrastructure


• Own and manage our cloud data warehouse


• Dealing with data governance, such as documentation, data integrity/quality and data security


• Build data pipelines (ETL, validation, automation, monitoring and logging...) that enable analysts and other stakeholders across the organization for data-focused product and data-driven business decisions


• Make changes on the existing data system to optimize and improve accuracy of the data process


• Give instruction or/and help stakeholder on the best practice to pull and use data


• Create scalable and actionable solutions, in the form of analytics, reports and dashboards for stakeholders to solve business and technical problems through ad-hoc requests


• Work closely with the data scientists to deploy and automate machine learning algorithms


• Write and revise technical documents and blogs, including design, development and application


• Complete data engineering projects with supervisor and guidance

A potential machine learning engineer track would require work in:

• Build scalable/robust MLOps infrastructure for continuous delivery of ML models


• Support Data Science team by building ML pipelines (auto-deploy, monitor, log)


• Create automated self-service procedures that allow ML models to scale across multiple business applications and entry points

Actual Work Location
2 Avenue de Lafayette, Boston, Massachusetts 02111-1750

All Locations
Lafayette City Center

Exempt / Not Exempt
Exempt



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