Specialist, Analytics Engineer
MSD (Merck & Co., Inc.) (company site, opens in a new tab)5+ yrsAndhra PradeshHybridDirect career pageEst. 18–30 LPA
Posted on 10 Sept
Reach people at MSD (Merck & Co., Inc.)
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at MSD (Merck & Co., Inc.) you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, develop, and support scalable analytical data products across Patient Support Services, Specialty Pharmacy, Hub Enrollment, and Aggregator data ecosystems.
- Translate requirements from product owners and analytics leads into robust data pipelines, models, and datasets to enable patient journey analytics including access, therapy initiation, adherence, and persistence.
- Ensure data quality, governance, compliance, and AI readiness while collaborating across cross-functional teams within Agile frameworks.
Minimum Requirements
- Minimum 5 years of experience in analytics engineering, data engineering, healthcare analytics, or related fields.
- Strong proficiency in SQL and Python; experience with cloud data platforms like Databricks, Snowflake, Azure, AWS, or Redshift.
- Experience working with patient-level healthcare data sets related to Patient Support Services, Specialty Pharmacy, Hub, or Aggregator data.
- VISA Sponsorship: No; Travel Requirements: Approximately 10%; Work Location: Hybrid; Work Experience Required: 5+ years.
Ideal Candidate Profile
- Experienced in healthcare data ecosystems, particularly patient access, specialty pharmacy networks, and patient journey analytics.
- Skilled in building enterprise-grade, compliant data products that support advanced analytics, reporting, and AI use cases in healthcare.
- Comfortable operating in Agile environments and collaborating with multiple stakeholders including product owners, analytics teams, and business units.