Senior Data Engineer

Tiger Analytics is an advanced analytics consulting firm focused on data science, machine learning, and artificial intelligence. The company is seeking an experienced Data Engineer to design, build, and maintain scalable AWS data pipelines and integration processes, while supporting analytics, machine learning, Generative AI, and pharmaceutical data initiatives.

Responsibilities:

• Design, develop, and deploy end-to-end data pipelines on AWS using services such as Amazon S3, AWS Glue, AWS Lambda, Amazon Redshift, and related data platform technologies
• Build and maintain scalable data processing and transformation workflows using Databricks, Apache Spark, and SQL
• Develop and maintain Apache Airflow workflows for pipeline orchestration, scheduling, dependency management, monitoring, and automation
• Integrate and process commercial pharmaceutical data sources such as Xponent, Veeva, MMIT, Plantrak, Specialty Pharmacy, LAAD, and similar sources
• Build and optimize data pipelines supporting pharma KPIs, metrics, analytics, and reporting requirements
• Design and implement data pipelines for AI/ML and Generative AI workloads, including structured and unstructured data preparation
• Enable data pipelines supporting LLM-based applications, vector embeddings, and knowledge retrieval/RAG solutions
• Support migration of legacy data systems and pipelines to modern AWS cloud and lakehouse architectures
• Monitor, troubleshoot, and optimize data pipelines for performance, scalability, reliability, and cost-effectiveness
• Ensure data pipelines meet required standards for data quality, accuracy, consistency, and operational reliability
• Communicate effectively with technical and business stakeholders to understand requirements and translate pharmaceutical business needs into scalable data solutions

Requirements:

• 8+ years of experience in Data Engineering, preferably with experience supporting commercial pharmaceutical/healthcare data environments
• Strong hands-on experience with AWS cloud, Databricks, Spark, and SQL
• Strong experience building ETL/ELT data pipelines and large-scale data processing workflows
• Hands-on experience with Apache Airflow for workflow orchestration
• Strong understanding of data modeling, data lake/lakehouse architecture, data ingestion, and transformation frameworks
• Deep knowledge of commercial pharmaceutical data sources: Xponent, Veeva, MMIT, Plantrak, Specialty Pharmacy, LAAD, and other commercial pharma data sources
• Strong understanding of pharmaceutical commercial data processes, including: Alignment, Allocation, Split credits, Market basket, Customer universe
• Strong understanding of pharma KPIs, metrics, and commercial analytics
• Strong analytical, problem-solving, and data troubleshooting skills