Trainee Data Solutions Engineer

About Milliman

Milliman is among the world's largest providers of actuarial and related products and services. Our mission is to serve our clients to protect the health and financial well-being of people everywhere. Founded in 1947, Milliman is an independent firm with offices in major cities around the globe. We are owned and managed by our principals—senior consultants whose selection is based on their technical, professional, and business achievements. Milliman serves the full spectrum of business, financial, government, union, education, and nonprofit organizations. In addition to our consulting actuaries, Milliman's body of professionals includes numerous other specialists, ranging from clinicians to economists.

About Milliman’s MedInsight Team

Leading with our core values of Quality, Integrity, and Opportunity, MedInsight is the healthcare industry’s most trusted solution for healthcare intelligence. Our company’s purpose is to empower easy, data-driven decision-making on important healthcare questions. Through our products, education, and services, MedInsight is making an impact on healthcare by helping to drive better outcomes for patients while reducing waste. Over 300 leading healthcare organizations have come to rely on MedInsight analytic solutions for healthcare cost and care management.

Job Description

As a Trainee Data Solutions Engineer, you will be introduced to various data engineering concepts, tools, and best practices. You will work closely with senior team members to support data integration, ETL processes, and analytics solutions. This is an excellent opportunity to gain hands-on experience with SQL, Azure Data services, and Power BI while developing a strong foundation in data engineering.

Job Duties

• Support the design, development, and maintenance of ETL processes to facilitate smooth and efficient data integration across multiple sources.
• Assist in building and refining data models that align with business objectives, ensuring accuracy and consistency in data representation.
• Learn and contribute to integrating structured and unstructured data from various internal and external sources into a centralized data repository.
• Write, optimize, and troubleshoot basic T-SQL queries, progressively working towards improving data retrieval efficiency and performance.
• Gain hands-on experience with Azure Data Factory and Azure Data Bricks, supporting large-scale data transformation, processing, and analytics workflows.
• Assist in monitoring and troubleshooting ETL workflows, SQL scripts, and data pipelines, identifying potential performance issues and proposing basic optimizations.
• Work on developing interactive Power BI reports and dashboards, learning to translate business requirements into meaningful visual insights under the guidance of senior team members.
• Engage with stakeholders, including data engineers and analysts, to understand data needs, document requirements, and contribute to data-driven decision-making
  
  

Requirements

• Solid understanding of SQL fundamentals, including relational database concepts, indexing, query optimization, and data manipulation techniques.
• Basic exposure to programming languages like Python or Scala, with a willingness to develop scripting skills for data processing and automation.
• Eagerness to explore cloud-based data engineering tools, particularly Azure Data Factory and Azure Data Bricks, and gain hands-on experience with ETL pipelines and big data processing.
• Strong analytical and problem-solving mindset, with the ability to identify patterns, troubleshoot data inconsistencies, and suggest improvements.
• Ability to adapt quickly to new challenges and technologies, demonstrating curiosity and enthusiasm for continuous improvement in data engineering.
  
  

Educational Requirements

• BE/B.Tech/BCA/MCA/MS in Computer Science, Information Technology, or a related field.
• Recent graduates or final-year students with relevant coursework/projects are encouraged to apply.
  
  

What We Offer

• Hands-on Experience: Work on real-world projects that use cutting-edge technologies like Databricks, PySpark, and Azure.
• Mentorship & Guidance: Receive mentorship from experienced professionals to help you grow in your data engineering career.
• Global Collaboration: Join a global team and gain experience working in a distributed environment.
• Learning Opportunities: Access to various training and development programs to enhance your skills and knowledge in the field of data engineering.