Analytics Engineering Solutions Architect

Overview

Responsible for designing and implementing advanced analytics solutions that drive data-driven decision-making across the organization. This position collaborates with cross-functional teams, including data engineers, data scientists, analytics engineers and business stakeholders, to understand requirements and translate them into robust analytical frameworks including self-service solutions in Epic, Health Catalyst and Azure Services.

Remote/work from home with strong preference for candidates residing within the UPH geographies of Iowa, Illinois, & Wisconsin.

Why UnityPoint Health?

At UnityPoint Health, you matter. We’re proud to be recognized as a Top 150 Place to Work in Healthcare by Becker's Healthcare several years in a row for our commitment to our team members.  

Our competitive Total Rewards program offers benefits options that align with your needs and priorities, no matter what life stage you’re in.  Here are just a few:      

• Expect paid time off, parental leave, 401K matching and an employee recognition program.   

• Dental and health insurance, paid holidays, short and long-term disability and more. We even offer pet insurance for your four-legged family members.  

• Early access to earned wages with Daily Pay, tuition reimbursement to help further your career and adoption assistance to help you grow your family.   

With a collective goal to champion a culture of belonging where everyone feels valued and respected, we honor the ways people are unique and embrace what brings us together.   

And, we believe equipping you with support and development opportunities is a vital part of delivering an exceptional employment experience. 

Find a fulfilling career and make a difference with UnityPoint Health.

Responsibilities

Design & Implementation of Analytics Solutions (40%) 

• Architect Scalable Analytics Solutions:
  • Responsible for designing and developing robust analytics frameworks that can handle large volumes of data and integrate seamlessly with existing systems and data sources
  • Selecting the right architecture to ensure scalability and performance
• Develop Data Models and ETL Processes:
  • Create and maintain data models that represent business metrics and dimensions effectively 
  • Design and oversee ETL (Extract, Transform, Load) processes that ensure data is accurately extracted from various sources, transformed as necessary, and loaded into analytical databases for easy access

Stakeholder Collaboration & Requirements Gathering (30%)

• Work with Business Stakeholders:
  • Collaborate closely with various business units to understand their analytics needs
  • Engage with stakeholders to gather detailed requirements, ensuring that the solutions designed align with business objectives
• Translate Requirements into Technical Specifications:
  • Take the gathered requirements and translate them into clear, actionable technical specifications for the development team, ensuring a mutual understanding between business needs and technical capabilities

Tool Evaluation & Technology Recommendations (20%) 

• Assess Analytical Tools and Technologies:
  • Evaluate different analytical tools and technologies, considering factors like performance, cost, and ease of use
  • Stay informed about the latest advancements in the field and understanding which tools best fit the organization’s needs
• Recommend Solutions:
  • Based on evaluations, provide recommendations for the most suitable analytical tools and technologies that can enhance the organization's analytics capabilities

Industry Trends & Presentations (10%) 

• Stay Current with Trends:
  • Responsible for keeping up with emerging technologies and trends in the analytics and data science fields
  • This includes researching new tools, methodologies, and best practices to ensure that the organization remains competitive and innovative
• Present Findings to Stakeholders:
  • Regularly present findings, insights, and recommendations to various stakeholders, translating complex technical concepts into clear and actionable insights that can drive informed decision-making across the organization

Qualifications

Education:  

• Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field required.  
• Master’s degree in a relevant field preferred. 

Experience: 

• Five (5) years of experience with SQL required. 
• Three (3) years of experience with Data Warehousing Python, R required.  
• Two (2) years of experience with data integration platforms and Cloud platforms (AWS, Azure, Google Cloud) required.  
• One (1) year experience with Cloud Storage (AWS S3, Azure Blob), Version Control (Git) and Agile Methodologies required.  
• Two (2) years of experience with Analytical tools and libraries (Pandas, NumPy, Scikit-Learn) and Statistical Analysis (SAS, SPSS) preferred. 
• One (1) year experience with API’s and Integration: RESTful APIs and API Tools (Postman, Apigee) preferred.  

License(s)/Certification(s): 

• None