Principal Data Engineer

About the Role:

BlinkRx is seeking a highly experienced Principal Data Engineer to design, build, and evolve our core data ecosystem. This role is for a hands-on technical leader who thrives on transforming raw, disparate data into trusted, well-modeled, and analytics-ready assets that power decision-making, AI, and operational systems across the company.

As a senior individual contributor within the Data organization, you will own critical aspects of data architecture, modeling, and transformation. You’ll play a central role in unifying data across domains, evolving schemas and pipelines as the business changes, and ensuring our data foundation supports real-time use cases, advanced analytics, and AI-driven initiatives.

What You Will Do:

Data Architecture & Modeling

• Design and maintain robust data models that clearly define structure, relationships, and business meaning across domains.
  
  
• Establish and evolve canonical data definitions, schemas, and dimensional models to support analytics, reporting, and AI use cases.
  
  
• Ensure data models balance scalability, performance, and ease of use for downstream consumers.
  
  

Data Transformation & Pipelines

• Build and own end-to-end data transformation workflows, from raw ingestion through curated, modeled, and reporting-ready layers.
  
  
• Implement scalable ELT/ETL patterns that support batch and real-time data processing.
  
  
• Continuously refactor and improve existing pipelines to increase reliability, performance, and clarity as requirements evolve.
  
  

Data Integration & Unification

• Integrate data from diverse internal and external sources into a unified, consistent data foundation.
  
  
• Resolve data duplication, inconsistency, and fragmentation across systems through thoughtful modeling and transformation.
  
  
• Partner with upstream and downstream teams to ensure clean contracts and reliable data interfaces.
  
  

AI & Advanced Analytics Enablement

• Design data structures and pipelines that enable AI, machine learning, and advanced analytics use cases.
  
  
• Ensure training, feature, and inference data are accurate, timely, and reproducible.
  
  
• Collaborate with analytics and data science partners to operationalize AI-ready datasets.
  
  

Change Management & Technical Stewardship

• Lead change management for data systems, including schema evolution, backfills, and migration strategies.
  
  
• Establish best practices for refactoring data pipelines without disrupting critical reporting or consumers.
  
  
• Act as a steward of long-term data quality, maintainability, and technical integrity.
  
  

Collaboration & Influence

• Partner closely with Analytics, Product, Finance, Operations, and Engineering to translate business needs into scalable data solutions.
  
  
• Communicate complex data concepts clearly to technical and non-technical stakeholders.
• Set standards and provide technical guidance to other data engineers through design reviews and mentorship.

Qualifications:

Minimum Requirements

• 10+ years of experience in data engineering or related software engineering roles.
  
  
• Deep expertise in SQL and data transformation frameworks, with strong proficiency in Python.
  
  
• Extensive experience designing and maintaining data models (dimensional, relational, and analytical).
  
  
• Proven track record of building reliable data pipelines from raw data to analytics and reporting layers.
  
  
• Experience integrating and unifying data across multiple systems and domains.
  
  
• Strong understanding of data quality, testing, observability, and lifecycle management.
  
  
• Excellent communication skills and ability to drive alignment through technical leadership rather than people management.
  
  

Preferred Qualifications

• • Experience supporting AI or machine learning workloads with production-grade data pipelines.
    
    
  • Background in healthcare, pharmacy, or other regulated data environments.
    
    
  • Experience with real-time or streaming data architectures.
    
    
  • Familiarity with modern cloud data stacks (e.g., cloud data warehouses, Spark-based processing).