Job description

Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world. We are seeking an experienced Data Engineer to join our data team. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines, data integration processes, and data infrastructure on AWS cloud. You will collaborate closely with data scientists, analysts, and AI teams to support analytics, machine learning, and Generative AI initiatives across the organization. Requirements Key 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. Required Skills: • 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. Benefits Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, challenging, and entrepreneurial environment, with a high degree of individual responsibility. Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

Requirements

Must-haves

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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaiawssqlmlllm

Platform: workable
Effort: medium