Overview

Greetings from GlobalData!!!

Responsibilities

• Design and build scalable ETL/ELT pipelines for ingesting, transforming, and loading pharmaceutical data from multiple sources
• Process indication-level market forecast data from Excel workbooks into structured JSON and DB-ready formats for frontend visualization
• Build and maintain data extraction pipelines from clinical trial registries, conference abstracts, and drug databases
• Implement multi-step validation workflows to ensure pipeline output accuracy and completeness before downstream handoff
• Design normalized relational schemas in SQL Server to support pharmaceutical analytics use cases
• Collaborate with Data Scientists on data preparation and feature engineering pipelines
• Maintain data dictionaries, schema documentation, and lineage records for all active pipelines
• Contribute to architecture decisions on data storage, processing strategies, and API integrations
• Own data quality across assigned pipelines implement automated checks, alerting, and audit trails
• Investigate and resolve silent data errors including missing fields, incorrect labels, zero-value nodes, and schema mismatches
• Write thorough unit and integration tests for all pipeline components
• Work within an agile delivery framework participate in sprint planning, estimation, reviews, and retrospectives
• Follow and contribute to team-wide GitHub standards: branching strategy, PR reviews, naming conventions, and documentation
• Actively use AI coding tools (GitHub Copilot, Claude, etc.) as part of everyday development
• Produce clear technical documentation and handoff notes for every delivery

Qualifications

• Python (5+ years, production pipelines)
• SQL Server (primary)
• SQL complex queries, stored procedures, optimisation
• Git / GitHub (branching, PRs, code review)
• ETL/ELT design and implementation
• Pandas, SQLAlchemy, Pydantic
• Data modelling and schema design
• Excel / openpyxl for source data handling
• REST API integration and data extraction
• Docker or containerised environments (desirable)
• Data validation and testing practices
• Selenium / web scraping tools (desirable)
• JSON / XML data processing
• Airflow or equivalent orchestration (desirable)

Nice to have

• Experience working with pharmaceutical or life sciences data clinical trials, market forecasting, drug pipelines, or therapy area analytics
• Familiarity with NLP pipelines or LLM-assisted data processing workflows
• Knowledge of multi-agent AI architectures or experience building pipelines that feed AI/ML models
• Experience building Streamlit or lightweight data exploration tools
• You think before you code research, design, and plan before diving into implementation
• You take end-to-end ownership of your pipelines from requirement to optimisation
• You are comfortable flagging issues early rather than discovering them post-handoff
• You write code that your teammates can read, understand, and maintain
• You embrace AI tools as a productivity multiplier, not a threat
• You contribute to a team culture of quality, transparency, and continuous improvement