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Design and document enterprise data architecture (conceptual, logical, physical), select storage solutions (warehouses, lakes, ODS), build ETL pipelines, ensure data quality and security, evaluate new data technologies, and collaborate with data engineers, scientists, and analysts to meet business data needs.

Customer-facing senior presales solution engineer supporting sales: translate customer business and technical needs into solutions, lead discovery sessions, demos and workshops, prepare technical proposals, present value to stakeholders, collaborate with sales and engineering, and address customer questions throughout the sales cycle.

Design, build, and improve LLM-based and RAG solutions, internal chatbot and conversational AI products. Work with structured and unstructured data, embeddings, and vector search. Optimize retrieval, prompts, model selection, and evaluation. Collaborate with product, engineering, data, and business teams and contribute to scalable, secure generative AI architectures.

Manage, monitor, optimize, and secure enterprise databases. Review and improve T-SQL and database objects before production, troubleshoot performance issues, perform root-cause analysis, and collaborate with development teams to enhance database design and application efficiency.

Maintain and modernize legacy ETL and data warehouse processes; develop with Oracle, Teradata, SQL/PLSQL and DataStage/Informatica/Ab Initio; build Apache Spark data pipelines; read/write AWS S3; lead legacy-to-modern migration, PoCs, architecture, and technical standards for data teams.

Lead enterprise accounts selling cloud, data and AI solutions. Manage full sales cycle, build C-level relationships, drive renewals/upsells, coordinate internal teams (BDRs, CSMs), use MEDDIC and CRM discipline to forecast and close deals, and develop account growth strategies aligned to client business goals.