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Designs and builds scalable data platforms and pipelines for scientific, clinical, manufacturing, and process-engineering data. Responsibilities include developing batch and streaming ETL/ELT systems, integrating Databricks, Snowflake, cloud platforms, MES, SCADA, and laboratory systems, and enforcing governance and GxP compliance. The role provides hands-on technical leadership, establishes data engineering standards, and collaborates with scientists, process engineers, and platform teams. It requires three days onsite weekly in Indianapolis and unrestricted U.S. work authorization.

Own the architecture and hands-on implementation of production MLOps systems, scalable inference services, model deployment pipelines, monitoring, and governance. Integrate machine learning into pharmaceutical manufacturing, process control, automation, and scientific or clinical applications. Collaborate with chemical engineers, computational biologists, and platform teams while establishing enterprise CI/CD and MLOps standards. This is a hands-on senior role requiring hybrid onsite work in Indianapolis.

Design and operate AI-ready data products, semantic layers, governed datasets, and natural-language analytics experiences on Snowflake or Databricks. Build ETL/ELT pipelines using dbt, Airflow, Snowpark, and PySpark; deploy Cortex or Genie capabilities; develop RAG and conversational analytics applications; and implement governance, security, lineage, auditability, and performance optimization. Partner with Finance stakeholders to define trusted KPIs, hierarchies, business glossaries, and semantic models in a regulated environment.

Build and deploy production AI applications for enterprise Finance teams, from stakeholder discovery through prototyping, regulated deployment, adoption, and ROI measurement. Develop agentic and RAG workflows using Snowflake Cortex or Databricks Genie, LLM orchestration frameworks, vector search, and evaluation harnesses. Integrate governed enterprise data and platforms while meeting security, compliance, and observability requirements. Advise CFO, FP&A, Controllership, and Treasury stakeholders, and create reusable technical assets.

Build and deploy production AI applications for enterprise Finance teams, from discovery with CFO stakeholders through prototyping, regulated deployment, adoption, and ROI measurement. Develop agentic and RAG workflows using Snowflake Cortex or Databricks Genie, LLM orchestration frameworks, vector search, and evaluation harnesses. Integrate governed enterprise data and platforms, ensure compliance and observability, and advise senior Finance leaders while creating reusable technical assets.

Own and prioritize product backlogs, define user stories and acceptance criteria, lead Agile ceremonies, approve releases, and translate business needs into technical solutions. Analyze workflows, integrations, data flows, and enterprise platforms while aligning products with banking regulations. Use SQL, Power BI, and Tableau for data validation and dashboards, and govern UAT, QA scenarios, and defect resolution. Serve as a strategic advisor to executives and clients across media, pharma, and financial technology initiatives.

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.