Design and operate governed, AI-ready data products, semantic layers, and natural-language analytics experiences on Snowflake and/or Databricks. Build ETL/ELT pipelines with dbt, Airflow, Snowpark, and PySpark; deploy Cortex or Genie capabilities; develop RAG applications; implement governance, security, lineage, and metadata controls; optimize platform and LLM costs; and partner with Finance stakeholders on trusted data products.
Our Client's Digital Finance IT is building an AI-enablement layer on top of our enterprise data platform to enable business users across Finance to interact with governed data in natural language. We're hiring a Data Enablement Engineer to design, build, and operate the trusted datasets, semantic models, and embedded AI experiences that make this possible. This is a data platform engineering role, not a data science or model-building role. You will spend your time engineering the data foundation that makes AI reliable — semantic layers, governed data products, and embedded natural-language analytics — not training models.
What You'll Do
- Design and build AI-ready data products on Databricks — trusted datasets with well-defined business semantics, KPIs, hierarchies, and business glossary alignment
- Implement semantic layers and governed datasets that support both traditional BI consumption and natural-language querying by business users
- Deploy and operate Databricks Genie spaces with Unity Catalog, tuning them for accuracy, adoption, and business relevance
- Build RAG pipelines and conversational analytics applications grounded in governed enterprise data — including Streamlit or Databricks Apps that let business users query data without writing SQL
- Engineer robust ETL/ELT pipelines (dbt, Airflow, PySpark) that produce and maintain the trusted data these AI experiences depend on
- Implement data governance — RBAC, row/column-level security, masking, lineage, auditability, catalog and metadata management — in a regulated pharma environment
- Optimize cost and performance on both the data platform side (warehouse sizing, cluster tuning, query optimization) and the AI side (token usage, caching, model routing)
- Partner with Finance business stakeholders to translate domain requirements into semantic models and governed data products they can trust
Requirements
Must-Have Experience
- 5+ years hands-on data engineering on cloud data platforms — Databricks demonstrated in real project delivery, not skill-list-only
- Direct hands-on experience with Databricks Genie — you have built, configured, and tuned these in production or advanced pilots, with specific reference to the flavors used (Genie spaces with semantic models)
- Semantic layer / trusted data product delivery — you have built governed datasets that business users can rely on, with KPI definitions, hierarchies, and business glossary alignment
- dbt, PySpark, SQL, Python — strong across the modern data stack
- Orchestration with Airflow, Databricks Workflows, or equivalent
- Data governance in regulated environments — RBAC, RLS, masking, lineage, auditability
- Experience integrating structured and unstructured data (PDFs, SharePoint/Teams content, enterprise knowledge sources) into AI-enablement workflows
Nice to Have
- Pharma, life sciences, or regulated financial services domain experience
- Veeva CRM, IQVIA, SAP, or clinical data source integration
- Streamlit or Databricks Apps for business-facing analytics
- Databricks Data Engineer Professional certification
- LangChain, LlamaIndex, or equivalent RAG frameworks
- Cost optimization on both compute (warehouse/cluster) and LLM (tokens/caching/routing) dimensions
What We're NOT Looking For
- Data Scientists — this role is not model training, fine-tuning, LoRA/RLHF, or ML research
- Pure Data Engineers who list Cortex or Genie as a skill but haven't shipped it in production
- AI/GenAI engineers whose center of gravity is LangChain agents or RAG-over-documents, without a strong governed data platform foundation
- Computer vision, NLP model builders, or multi-agent orchestration specialists — wrong shape for this role
Similar Jobs
Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical
Leads health technology assessment, value, and evidence strategy for Pfizer’s genitourinary oncology portfolio. Manages HEOR, real-world evidence, economic models, global value dossiers, registries, and evidence dissemination to support reimbursement and patient access. Partners with global, regional, country, and cross-functional oncology teams, oversees vendors and project teams, and communicates findings through publications and conferences.
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Drive new SaaS business revenue through account and territory planning, prospect research, business development, and field sales. Build C-suite relationships, coordinate account strategies across cross-functional teams, advise customers on IT roadmaps, identify specialist resources, negotiate deals, achieve sales targets, and maintain trusted client relationships.
Top Skills:
AISaaSServicenow Ai Platform
Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical
Leads Pfizer’s environmental sustainability strategy across climate, Scope 3 emissions, biodiversity, nature, product sustainability, and sustainable sourcing. Manages supplier decarbonization programs, sustainability metrics, regulatory horizon scanning, risk assessments, governance, training, and communications. Facilitates cross-functional teams, advises stakeholders, represents Pfizer in external initiatives, and serves as a sustainability subject matter expert across the enterprise.
Top Skills:
Microsoft TeamsWebex
What you need to know about the Pune Tech Scene
Once a far-out concept, AI is now a tangible force reshaping industries and economies worldwide. While its adoption will automate some roles, AI has created more jobs than it has displaced, with an expected 97 million new roles to be created in the coming years. This is especially true in cities like Pune, which is emerging as a hub for companies eager to leverage this technology to develop solutions that simplify and improve lives in sectors such as education, healthcare, finance, e-commerce and more.
