Senior Data Engineer
T
Tiger AnalyticsData Analytics
Chicago, Illinois, United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- AWSSQLApache AirflowETLMachine LearningData engineeringSparkDatabricksGenerative AI
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.
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.
- Design and implement data pipelines for AI/ML and Generative AI workloads, including structured and unstructured data preparation.
- Build and optimize data pipelines supporting pharma KPIs, metrics, analytics, and reporting requirements.
- 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.
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