Senior Data Engineer: AWS, AI Pipelines & Pharma Data

Tiger Analytics Inc.; Chicago (IL); On-site; USD 120,000 - 160,000; Full time

Job summary

Tiger Analytics is seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines on AWS, collaborating with data scientists and AI teams.

You will work with S3, Glue, Lambda, Redshift, Databricks, Spark, and Airflow to support pharma data sources like Xponent, Veeva, MMIT, and Plantrak. This role offers a challenging, entrepreneurial environment with growth in advanced analytics and Generative AI initiatives.

Qualifications

• 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.
• 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 pharma KPIs, metrics, and commercial analytics.
• Strong analytical, problem-solving, and data troubleshooting skills.

Responsibilities

• Design, build, 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.

Skills

AWS

Databricks

Apache Spark

SQL

Airflow

ETL/ELT

Data modeling

Pharma data

Data pipelines

Problem solving

Tools

Redshift

S3

Glue

Lambda

Job description

Tiger Analytics is seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines on AWS, collaborating with data scientists and AI teams.

You will work with S3, Glue, Lambda, Redshift, Databricks, Spark, and Airflow to support pharma data sources like Xponent, Veeva, MMIT, and Plantrak. This role offers a challenging, entrepreneurial environment with growth in advanced analytics and Generative AI initiatives.