Lead Product AI Data Engineer & Architect

About You – experience, education, skills, and accomplishments  

• Bachelor’s degree in engineering or master’s degree (BE, ME, B Tech, MTech, MCA, MS) 

• Minimum 7+ 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 had

• Experience with cloud data warehouses such as 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   

You will be joining a team responsible for creating and maintaining internal tools which allows the company to take unstructured data available on the internet into structured data which can then be cross referenced and analyzed. The data will be exposed to multiple products which are in term provided to our customers. You will be interacting with other teams in creating a service mesh structure which communicate through asynchronous queue services hosted in AWS.

Hours of Work

• This is a hybrid role working 2-3 days a week in the Bangalore, India Clarivate office