Job Description :

We are seeking a Principal Product AI Data Engineer / Architect to define, build, and scale enterprise-grade AI-ready data platforms for the Life Sciences and Healthcare (LSH) ecosystem.

About You :

Experience, Education, Skills, and Accomplishments :

- 10+ 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.

- Strong ability to influence technical outcomes through architectural leadership and enterprise strategy.

It would be great if you also have :

- 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 will be a big plus.

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.