We are seeking a Senior Product AI Data Engineer / Architect to define, build, and scale enterprise-grade AI-ready data platforms for the Life Sciences and Healthcare (LSH) ecosystem. This role combines hands-on technical mastery with enterprise architecture leadership, influencing multiple product lines and setting long-term standards for dimensional modeling, analytics pipelines, and AI data enablement.
About you education and Qualifications
5+ years of professional experience in Data Engineering, Analytics Engineering, or Data Architecture.
Proven experience in enterprise-scale data architecture and distributed pipeline design. Expert-level proficiency in SQL and relational database design.
Strong hands-on experience in Python for pipeline automation, orchestration, and data framework development.
Deep expertise in dimensional modeling, including star and snowflake schemas, fact/dimension tables, SCDs, surrogate keys, and hierarchical dimensions.
Experience designing and operating production-grade ETL/ELT pipelines for analytics and AI/ML workloads and strong ability to influence technical outcomes through architectural leadership and enterprise strategy.
What will you be doing in this role:
Experience with cloud data warehouses: Snowflake, Databricks, BigQuery.
Familiarity with modern data orchestration and transformation tools: dbt, Airflow, Fivetran, Segment.
Experience handling semi-structured and event-driven data (JSON, logs, clickstream).
Exposure to BI and visualization tools: Power BI, Tableau, Looker, SAP BusinessObjects.
Experience with AWS, Azure, or GCP, including data governance, security, and compliance frameworks.
Background in Life Sciences or Healthcare analytics.
What will you be doing in this role
Define and evolve enterprise-level product data architecture across multiple product lines, ensuring scalability, reliability, and AI/ML readiness.
Architect scalable ETL/ELT pipelines and distributed data workflows for analytics, AI, and product intelligence.
Develop and enforce dimensional data modeling standards (star schemas, snowflake schemas) across the organization.
Design and maintain fact and dimension tables, ensuring proper grain, SCD handling, hierarchical dimensions, and high-performance queries.
Establish data architecture principles, naming conventions, and best practices for ETL/ELT, event tracking, and AI pipelines.
Serve as the technical authority guiding architecture decisions to meet product, platform, and AI requirements
AI & Product Data Enablement
Partner with cross-functional teams to translate requirements into highly scalable, analytics- and AI-ready data models and pipelines.
Curate and validate datasets for machine learning, experimentation, and advanced analytics.
Evolve event-driven architectures to align with dimensional modeling and downstream analytics.
Establish feature store frameworks and reusable AI data pipelines across multiple products.
About the Team
You will be part of Clarivate’s Life Sciences & Healthcare (LSH) Commercial Technology organization, working at the intersection of GenAI, applied AI, data, and product enablement. The team focuses on translating advanced AI capabilities into enterprise-ready SaaS applications that support data- and workflow-intensive products used by global pharma, biotech, and healthcare customers. The team is globally distributed across India and the U.S., operating in a hybrid model.
Hours of Work
Full-time, IST
40 hours per week
Hybrid working environment
At Clarivate, we are committed to providing equal employment opportunities for all
qualified
persons with respect to hiring, compensation, promotion, training, and other terms, conditions, and privileges of employment. We comply with applicable laws and regulations governing non-discrimination in all locations.