Position Summary
Bridge data engineering and AI engineering for Commercial Pharma datasets. The role builds AI-ready data pipelines, embedding pipelines, vector stores, feature-ready datasets and metadata integrations needed to power GenAI, semantic search and agentic analytics use cases.
Business / Program Context
The role is part of a Vizag-based delivery capability supporting Commercial Pharma datasets across global markets. The work may include platform operations, data engineering, data quality, reporting, AI enablement, release governance and stakeholder support for enterprise commercial data products. The role is not limited to Japan-specific datasets; Japan market familiarity may be useful but is not a mandatory baseline requirement for most positions.
Job Responsibilities
- Prepare AI-ready commercial datasets using Databricks, SQL, Python and PySpark.
- Build embedding pipelines, vector database ingestion and semantic search indexes.
- Create metadata, lineage, chunking, retrieval and refresh processes for enterprise knowledge sources.
- Support RAG solution development, evaluation datasets and AI application integration.
- Implement data quality, access control, monitoring and refresh logic for AI pipelines.
- Work with AI Engineers, Data Engineers, Platform Engineers and governance teams to productionize AI use cases.
- Work as part of a Vizag-based delivery team supporting Commercial Pharma datasets and global commercial data stakeholders.
- Collaborate with business, technology, governance, quality and offshore/onshore delivery teams across time zones.
- Follow Agile delivery, SDLC, documentation, release, validation, access-control and data-governance standards.
- Contribute to knowledge management, reusable assets, SOPs, runbooks and operational excellence initiatives.
- Maintain strong communication discipline, including status updates, risk identification, issue escalation and timely stakeholder follow-through.
Required Work Experience
- 3–7 years of relevant experience aligned to the role and seniority level.
- Prior experience in pharmaceutical, life sciences, healthcare analytics, commercial data platforms or enterprise data programs.
- Experience working in a global delivery model with structured governance, documentation, SLAs and stakeholder communication.
- Ability to work from Vizag and collaborate effectively with India, global and client teams.
Technical / Functional Skills
- Databricks data engineering
- Python/PySpark and SQL
- Vector DB / embedding pipelines
- RAG data preparation
- Delta Lake and curated data design
- APIs and integration
- Data governance and access controls
- GenAI fundamentals
Commercial Pharma Dataset Exposure
- Pharmaceutical commercial data platforms
- Sales, prescription, claims, CRM and omnichannel datasets
- Customer master, HCP/HCO, product master, territory and alignment datasets
- Commercial KPIs, field force effectiveness, market share and promotional effectiveness
- Data quality, reconciliation, lineage, metadata and controlled release processes
Desirable Skills
- Experience supporting global pharma companies or commercial data programs.
- Exposure to Eisai-like commercial operating models, field reporting, sales operations and analytics use cases.
- Japan market dataset exposure is preferred but not mandatory unless the assignment requires direct Japan business engagement.
- Strong written and verbal communication skills; ability to explain data, risks, defects and business impact clearly.
- Certifications in Databricks, Informatica IDMC, Azure/AWS, Scrum, ITIL, Tableau/Power BI or AI/ML are preferred depending on role.
Success Measures / KPIs
- Reliable AI-ready data products, reusable retrieval pipelines and strong integration between commercial data and AI applications.
Indicative 30 / 60 / 90 Day Expectations
- First 30 days: complete onboarding, understand commercial data landscape, delivery processes, documentation standards and role-specific tools.
- First 60 days: independently contribute to assigned workstreams, support issue resolution, produce required artifacts and participate in governance forums.
- First 90 days: demonstrate measurable ownership, improve delivery quality, identify improvement opportunities and support stable operations or project outcomes.
