Senior Data Engineer: AWS, Databricks & Pharma Pipelines

Tiger Analytics; Chicago (IL); On-site; USD 130,000 - 180,000; Full time

Job summary

Tiger Analytics is seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines and data infrastructure on AWS. You will collaborate with data scientists, analysts, and AI teams to support analytics, machine learning, and Generative AI initiatives across the organization.

The role focuses on integrating pharma data sources, building robust pipelines, and enabling AI/LLM workloads while migrating legacy systems to modern lakehouse architectures.

Qualifications

• 8+ years of Data Engineering experience, preferably in pharma/healthcare data environments.
• Strong hands‑on experience with AWS, Databricks, Spark, and SQL.
• Experience building ETL/ELT pipelines and large‑scale data processing workflows.
• Hands‑on experience with Apache Airflow for workflow orchestration.

Responsibilities

• Design, develop, and deploy end‑to‑end data pipelines on AWS (S3, Glue, Lambda, Redshift).
• Build and maintain scalable data processing workflows with Databricks, Spark, and SQL.
• Develop Airflow workflows for orchestration, scheduling, monitoring and automation.

Skills

AWS

Databricks

Spark

SQL

Airflow

Data modelling

Pharma data

Data pipelines

Tools

Amazon S3

Redshift

Airflow

Job description

Tiger Analytics is seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines and data infrastructure on AWS. You will collaborate with data scientists, analysts, and AI teams to support analytics, machine learning, and Generative AI initiatives across the organization.

The role focuses on integrating pharma data sources, building robust pipelines, and enabling AI/LLM workloads while migrating legacy systems to modern lakehouse architectures.