Specialist, Analytics Engineer

Merck; Hyderabad; On-site; INR 1,200,000 - 2,100,000; Full time

Job summary

Merck in Hyderabad is seeking an Analytics Engineer to design, develop, and support data products that enable patient journey analytics and AI-ready insights. You will work with Data Product Owners, analytics teams, data engineers, and business stakeholders to transform healthcare data into trusted datasets for reporting, decision making, and global healthcare outcomes.

You will contribute to data quality, governance, and scalable pipelines across Hub, Specialty Pharmacy, Aggregator, and related

Responsibilities

• Hands-on development of last-mile data products using up-to-date technologies and DevOps practices.
• Build and maintain analytical data products supporting Patient Support Services, Specialty Pharmacy, Hub Enrollment, and Patient Journey analytics.
• Develop scalable data pipelines, automated data models, curated datasets, and semantic layers for reporting, analytics, data science, and AI use cases.
• Translate requirements and user stories from Data Product Owners and Lead Analytics Engineers into development activities across design, build, testing, deployment, and support.
• Integrate patient-level data across multiple health care resources and create reusable, business-ready datasets with consistent definitions and measures.

Job description

Job Description

Based in Hyderabad, join a global healthcare biopharma company and be part of a 130‑year legacy of success backed by ethical integrity, forward momentum, and an inspiring mission to achieve new milestones in global healthcare.

Be part of an organization driven by digital technology and data‑backed approaches that support a diversified portfolio of prescription medicines, vaccines, and animal health products.

Drive innovation and execution excellence. Be a part of a team with passion for using data, analytics, and insights to drive decision‑making, and which creates custom software, allowing us to tackle some of the world's greatest health threats.

Our Technology Centers focus on creating a space where teams can come together to deliver business solutions that save and improve lives. An integral part of our company's IT operating model, Tech Centers are globally distributed locations where each IT division has employees to enable our digital transformation journey and drive business outcomes. These locations, in addition to the other sites, are essential to supporting our business and strategy.

A focused group of leaders in each Tech Center helps to ensure we can manage and improve each location, from investing in growth, success, and well‑being of our people, to making sure colleagues from each IT division feel a sense of belonging to managing critical emergencies. And together, we must leverage the strength of our team to collaborate globally to optimize connections and share best practices across the Tech Centers.

Data Strategy Products and Solutions, Digital Human Health (DHH)

About the Role

Our Digital Human Health (DHH) organization is transforming how data is leveraged to improve patient outcomes, enhance healthcare experiences, and enable data‑driven decision‑making across Human Health. Within DHH, the Data Strategy, Products & Innovation (DSPI) team builds scalable data products that power analytics, reporting, advanced insights, and AI‑driven solutions across the patient support ecosystem.

As an Analytics Engineer, you will design, develop, and support analytical data products that provide visibility into the end‑to‑end patient journey across Patient Support Services (PSS), Specialty Pharmacy (SP), Hub Operations, Hub Enrollment, and Aggregator data ecosystems. You will partner with Data Product Owners, analytics teams, data engineers, and business stakeholders to transform complex healthcare data into trusted, scalable, and business‑ready datasets supporting patient access, therapy initiation, fulfillment, adherence, persistence, and patient support analytics.

Primary Responsibilities

Data Product Development and Engineering

• Hands‑on development of last‑mile data products using the most up‑to‑date technologies and software / data / DevOps engineering practices
• Build and maintain analytical data products supporting Patient Support Services, Specialty Pharmacy, Hub Enrollment, and Patient Journey analytics.
• Develop scalable data pipelines, automated data models, curated datasets, and semantic layers for reporting, analytics, data science, and AI use cases.
• TranslaterequirementsanduserstoriesfromDataProductOwnersandLeadAnalyticsEngineersintodevelopmentactivitiesacrossdesign,build,testing,deployment,andsupport.
• Integratepatient‑leveldataacrossmultiplehealthcaresourcesandcreatereusable,business‑readydatasetswithconsistentdefinitionsandmeasures.

Patient Journey and Healthcare Analytics

• Enableanalyticsacrossthepre‑andpost‑prescriptionpatientjourneyincludingenrollment,benefitsverification,priorauthorization,therapyinitiation,dispenseandfulfillment,adherence,persistence,andpatientsupportprogramengagement.
• Support datasets and measures that provide insight into patient access barriers, enrollment‑to‑treatment conversion, time to therapy, therapy progression, abandonment, discontinuation, and patient retention.
• Experience on working with data designing and integrations for Affordability (Copay/PAP/Bridge, Quick Start) and Adherence (Nurse Navigator, Digital Platforms)
• Develop working knowledge of Hub, Specialty Pharmacy, Aggregator, claims, and patient services data, including source‑specific limitations, business rules, and reconciliation needs.
• Partner with analytics and business teams to define and standardize patient journey milestones, KPIs, cohorts, and business definitions.

Data Quality, Governance and AI Readiness

• Ensure data products are developed with governance, privacy, compliance, quality, lineage, and documentation by design.
• Perform data profiling, validation, reconciliation, and root‑cause analysis across Hub, Specialty Pharmacy, Aggregator, and related patient‑level data sources.
• Ensure data products are trusted, scalable, reusable, and fit for analytical and AI‑driven consumption.
• Collaborate with cross‑functional product squads and communicate data risks, dependencies, a