Job Description :

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 :

- Bachelors degree in engineering or masters degree (BE, ME, B Tech, MTech, MCA, MS)

- 5+ years of professional experience in data engineering, analytics engineering, or data architectureheavy 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.