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Engaged Employer

GlaxoSmithKline Pharmaceuticals

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4.1

based on 1.8k Reviews

Video summary

1 GlaxoSmithKline Pharmaceuticals Data Engineer Job

Full Stack Data Engineer

GlaxoSmithKline Pharmaceuticals Limited

4.1

based on 1.8k Reviews

5-7 years

₹ 23 - 26L/yr (AmbitionBox estimate)

Bangalore / Bengaluru

1 vacancy

Full Stack Data Engineer

GlaxoSmithKline Pharmaceuticals

posted 8d ago

Job Role Insights

Flexible timing

Key skills for the job

Python SQL Healthcare Procurement SAP Business Intelligence CCTV Monitoring

+ 2 more

Job Description

We are seeking a highly skilled and experienced full stack data engineer to join our team to help support the US Specialty Business Unit. If you are passionate about leveraging cutting-edge cloud technologies and data engineering best practices to drive insights in the healthcare sector, we encourage you to apply.

Responsibilities

Design & Build Robust Data Pipelines: Develop, implement, and maintain end-to-end data pipelines that efficiently ingest, process, and transform large-scale pharmaceutical datasets, with a focus on anonymized patient-level datasets (APLD) and prescriber/account level datasets (e.g. Xponent, Drug Distribution Data).
Data Integration & ETL Processes: Collaborate with cross-functional teams to architect and optimize ETL processes, ensuring seamless integration of various data sources into centralized Azure environments (e.g., Azure Data Lake Gen2, Azure Data Factory).
Cloud & Microservices Implementation: Leverage Azure Cloud services to build scalable, secure, and high-performance data solutions. Develop RESTful APIs and microservices to facilitate data access and support business intelligence needs.
Quality Assurance & Data Governance: Ensure data accuracy, consistency, and security through rigorous testing, monitoring, and adherence to data governance standards. Collaborate with data governance teams to implement and maintain data cataloging practices.
Collaboration & Continuous Improvement: Work closely with data scientists, and Insights & Analytics teams to support advanced analytics and machine learning initiatives. Participate in code reviews, CI/CD pipeline development, and continuous process improvements to enhance overall system performance.

Basic Qualifications

Experience: 5-7 years of recent, hands-on experience in data engineering or a related field, with a proven track record of building robust data pipelines.
Education: Bachelor s Degree (or higher) in Computer Science, Engineering, or a related discipline.

Technical Proficiency:

Demonstrated experience with distributed computing platforms (e.g. Databricks), to process and analyze large datasets.
Proficient in programming languages and frameworks such as Python, SQL, and PySpark.
Strong experience with Azure Cloud environments, including Azure Data Lake Gen2, Azure Data Factory, and SQL Data Warehouse.
Solid understanding of ETL processes and data integration best practices.

Domain Experience: Understanding of pharmaceutical industry datasets (e.g., APLD) or similar large-scale healthcare datasets (Electronic Health Records).
Development Practices: Experience with microservices architecture, REST API development, version control (Git), and CI/CD pipelines (e.g., Azure DevOps).
Problem-Solving & Collaboration: Demonstrated ability to troubleshoot complex data issues, optimize system performance, and work effectively within a team environment.

Preferred Qualifications

Industry Expertise: Prior experience within the pharmaceutical or healthcare industry is highly desirable.
Data Visualization & Reporting: Exposure to traditional visualization/reporting tools (e.g., Power BI, Tableau) and GenAI tools (e.g. Databricks Genie) to support data-driven decision-making. Experience with developing semantic data models for visualization tools is a plus.
Machine Learning & MLOps: Basic understanding of machine learning workflows and experience with ML pipelines or MLOps practices is a plus.
Additional Data Technologies: Knowledge of other data storage and processing technologies (e.g., Vector databases, Cosmos DB, Redis, Elasticsearch) is beneficial, though not the primary focus.
Cloud & Container Technologies: Experience with container orchestration platforms (e.g., Kubernetes) and additional cloud services to further enhance scalable deployments is beneficial, though not the primary focus.