About this role
We are seeking a Senior Product AI Data Engineer to design, build, and operate robust data foundations that enable product analytics and AI-driven capabilities. This role is intended for professionals who have demonstrated the ability to independently deliver reliable data pipelines and contribute meaningfully to analytics and machine learning initiatives.
The successful candidate will work closely with Product Managers, Software Engineers, Data Analysts, and Data Scientists to ensure that product and AI-related data is accurate, scalable, and readily accessible to support data-informed decision-making and intelligent product features.
About You – experience, education, skills, and accomplishments
2+ years of relevant experience in data engineering, analytics engineering, software engineering, or related disciplines
Strong proficiency in SQL and relational database design
Hands-on programming experience in Python for data processing and pipeline development
Practical experience with data modeling concepts, including star schema and snowflake schema
Solid understanding of ETL/ELT processes, data validation, and data quality management
Experience supporting product analytics and AI/ML data workflows in production environments
Strong analytical and problem-solving skills with the ability to work independently
Effective written and verbal communication skills and experience collaborating with cross-functional teams
It would be great if you also have:
Experience working with cloud-based data warehouses such as Snowflake, BigQuery, or Amazon Redshift
Familiarity with modern data engineering and analytics tools (e.g., dbt, Airflow, Fivetran, Segment)
Experience supporting machine learning workflows, feature engineering, or model data pipelines
Hands-on experience with event-based and semi-structured data (e.g., JSON, application logs, clickstream data)
Working knowledge of business intelligence and data visualization tools such as Power BI, Tableau, or SAP BusinessObjects
Exposure to cloud platforms (AWS, Azure, or GCP) and basic data security principles
What will you be doing in this role?
Design, develop, and maintain scalable data pipelines supporting product analytics, user behavior tracking, and AI/ML use cases
Prepare, validate, and curate high-quality datasets for analytics, reporting, and machine learning models
Own and improve product event tracking implementations to ensure data accuracy, consistency, and completeness
Transform raw and semi-structured data into well-structured, analytics- and ML-ready datasets
Develop, optimize, and review complex SQL queries for dashboards, reporting, and model inputs
Monitor data quality, pipeline performance, and data freshness; proactively identify and resolve issues
Troubleshoot and resolve data-related issues impacting analytics, experimentation, or AI-driven features
Create and maintain clear documentation for data pipelines, data models, and metric definitions
Collaborate with data scientists to support feature engineering and AI data requirements
Contribute to data engineering best practices, standards, and process improvements
Works closely with QA, DevOps, Product Owners, Business Analysts and Project Management.
Adheres to Agile development practices and aid the team in doing so
Reliable, scalable product and AI data pipelines
High-quality datasets that enable analytics, experimentation, and machine learning
Improved data quality, observability, and trust across product teams
Best practices for data modeling, pipeline development, and documentation
About the Team
The MAI is a part of the ‘HDS’ within the Life Sciences division of Clarivate. The Market Access Intelligence provides a comprehensive view into the elaborate managed markets landscape with a complete, clear, and accurate picture of lives and affiliations by payer, pharmacy benefit manager (PBM), and employer. Trusted by government agencies and the largest health insurers in the U.S. to track their own performance, the Market Access Intelligence provides concrete, reliable data that forms foundational data for any successful market access strategy. Medical and pharmacy covered lives data at the national, state, metropolitan statistical area, and county levels to better inform strategic planning and resource allocation.
The ~8 Technology team members work closely with Product and Project Management across a number of solutions. As part of the wider Life Sciences organization in Clarivate, the team is looking to move to the next level of maturity and scale.
Hours of Work
Fulltime in IST zone
40 h/week
Hybrid working model
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.