We are seeking a Lead Product AI Data Engineer to design, build, and optimize end-to-end data architecture and scalable data platforms that power product analytics and AI-driven capabilities. This role is intended for highly experienced data engineers with 5+ years of experience and deep expertise in data architecture, dimensional data modeling, analytics architecture, and AI-ready data pipelines.
In this role, you will operate as a senior individual contributor and technical lead, owning product-level data architecture decisions, complex data models, and pipelines while influencing technical direction through hands-on leadership and mentorship. You will collaborate closely with Product Managers, Data Scientists, Analysts, and Platform Engineers to ensure product and AI data is accurate, well-architected, scalable, and analytics-ready.
We are building the next generation of AI-powered analytics, insights, and decision-support platforms for the global Life Sciences and Healthcare (LSH) community—supporting mission-critical use cases such as drug discovery acceleration, treatment effectiveness analysis, real-world evidence generation, and patient journey insights.
This role blends data architecture ownership, deep technical execution, dimensional modeling excellence, and team-level technical leadership
About You – experience, education, skills, and accomplishments
Bachelor’s degree in engineering or master’s degree (BE, ME, B Tech, MTech, MCA, MS)
5+ years of professional experience in data engineering, analytics engineering, or data architecture–heavy roles.
Expert-level proficiency in SQL and relational database design.
Strong programming experience in Python for data pipelines and automation.
Deep hands-on experience with data architecture and dimensional data modeling, including star schemas, snowflake schemas, fact tables, and dimension tables.
Strong understanding of slowly changing dimensions (SCDs), surrogate keys, grain definition, and hierarchical dimensions.
Experience designing and operating ETL/ELT pipelines for production analytics and AI/ML workloads.
Ability to influence technical outcomes through architectural leadership and collaboration.
It would be great if you also have:
Experience with cloud data warehouses such as StarRocks, Snowflake, Data Bricks, BigQuery, or Amazon Redshift.
Familiarity with tools such as dbt, Airflow, Fivetran, and Segment.
Experience working with event-driven or semi-structured data (JSON, logs, clickstream).
Exposure to BI and visualization tools (Power BI, Tableau, SAP BusinessObjects).
Familiarity with AWS, Azure, or GCP, including data governance and security best practices.
What will you be doing in this role?
Data Architecture & Technical Leadership
Own and evolve product-level data architecture, ensuring scalability, reliability, and alignment with analytics and AI/ML use cases.
Design and implement scalable, reliable data pipelines supporting product analytics, user behavior tracking, and AI/ML initiatives.
Define and maintain enterprise-aligned dimensional data models (star and snowflake schemas).
Design and maintain fact and dimension tables, ensuring correct grain, performance, and consistency.
Contribute to and help enforce data architecture, modeling standards, naming conventions, and ETL/ELT best practices within product teams.
Provide architectural guidance to ensure data solutions align with product requirements, platform constraints, and AI/ML needs.
Product & AI Data Enablement
Partner with Product Managers, Data Scientists, Analysts, and Engineers to translate requirements into well-architected data models and pipelines.
Prepare, validate, and document datasets used for analytics, experimentation, and machine learning.
Support and evolve product event tracking architectures, ensuring alignment with dimensional models and downstream analytics.
Data Quality, Reliability & Operations
Implement monitoring, testing, and alerting for data quality, pipeline health, and freshness.
Ensure integrity of fact and dimension data through validation, reconciliation, and automated checks.
Diagnose and resolve complex data issues affecting analytics, AI workflows, or product features.
Mentorship & Collaboration
Mentor and support data engineers through architecture reviews, code reviews, and design discussions.
Participate in cross-team data architecture, modeling, and pipeline design reviews.
Collaborate with platform, cloud, and security teams to ensure scalable, secure, and production-ready data architectures
Well-architected, enterprise-grade product and AI data platforms.
Analytics- and AI-ready datasets built on strong dimensional and architectural foundations.
Consistent application of data architecture, modeling, and data quality standards within product teams.
Technical mentorship that raises the bar for data architecture and engineering excellence.
About the Team
The DIA team operates globally and collaborates closely with product owners, architects, and data experts. We thrive on innovation, diversity, and a mission-driven culture focused on enabling life-changing insights.
Team comprising front-end and back-end engineers, QA, UX designers, product managers, and DevOps specialists. The team is focused on delivering high-quality editorial and data intelligence solutions, with a strong emphasis on innovation, ownership, and cross-functional collaboration.
This is a fast-paced, innovation-driven environment where you’ll have the opportunity to take ownership of features, contribute to system design, and collaborate with talented peers on impactful projects.
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.