As a Senior Lead Engineer you will architect the highly scalable, cloud-native data platforms that power our Disease Intelligence Analytics Productscritical tools that help researchers, clinicians, scientists, and business leaders make faster, more confident decisions. Youll help build the engine behind products used to accelerate drug discovery, evaluate treatment effectiveness, model patient journeys, and bring life-saving innovations to market.

This is an opportunity to build systems that not only drive next-generation AI, but also create measurable impact in healthcare and life sciences globally

If you're passionate about building enterprise-scale platforms, modern UI frameworks, microservices, and AI-ready applicationsthis role is for you.

About You experience, education, skills, and accomplishments :

- 7 years of professional experience in data engineering, analytics engineering, or related disciplines

- Expert-level proficiency in SQL and relational database concepts

- Strong programming experience in Python for data pipeline development and automation

- Deep hands-on experience in dimensional data modeling, including star schema, snowflake schema, fact tables, and dimension tables

- Knowledge of slowly changing dimensions (SCDs), surrogate keys, grain definition, and hierarchical dimensions

- Experience designing, building, and maintaining ETL/ELT pipelines for production analytics and AI/ML data workflows

- Proven ability to lead technical initiatives and influence cross-functional teams

- Strong analytical, problem-solving, and communication skills

It would be great if you also have :

- Familiarity with modern data engineering tools and orchestration frameworks (dbt, Airflow, Fivetran,

Segment)

- Experience implementing dimensional models using dbt or similar transformation frameworks

- Knowledge of event-driven or semi-structured data (JSON, logs, clickstream)

What will you be doing in this role?

- Lead the design and implementation of scalable, reliable data pipelines supporting product analytics, user behavior tracking, and AI/ML initiatives

- Own enterprise-level dimensional data modeling, including star schema and snowflake schema designs

- Define and enforce standards for data modeling, naming conventions, and ETL/ELT best practices across teams

- Provide strategic input on data architecture, ensuring alignment with product and AI/ML use cases

- Familiarity with modern data engineering tools and orchestration frameworks (dbt, Airflow, Fivetran, Segment)

- Establish monitoring, testing, and alerting frameworks for pipeline performance, data quality, and data freshness

- Ensure integrity of fact and dimension data through reconciliation, validation, and automated quality checks

- Troubleshoot and resolve complex data issues impacting analytics, AI workflows, or product features

- Partner with Product Managers, Data Scientists, Analysts, and Software Engineers to translate business and AI requirements into high-quality data models and pipelines

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 youll 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