Dataiku

Financial Planning and Analysis

• Take full ownership of NetSuite administration
• Ramp up and expand into Coupa and Navan
• Diagnose and resolve system issues end-to-end
• Execute finance system projects
• Maintain rigorous change controls
• Build trusted relationships across the business
• Get involved in innovation

Financial Systems Manager

SDET

• Ensure highest-quality products delivery
• Coding automated test suites
• Define manual testing plans and strategies
• Collaborate with developers to ensure code is testable
• Improve existing test infrastructure and frameworks
• Mentor new QA team members

Software Engineer in Test

Account Executive

• owning a territory with prospects and customers across a variety of industries
• managing the entire sales cycle
• collaborating cross-functionally with marketing and product management teams
• demonstrating the business value of Dataiku’s platform, DSS

Strategic Account Executive – Central/West

LLM Engineer

• Design end-to-end AI solutions on Dataiku's platform, leveraging Dataiku Agent Hub, Prompt Studio, LLM Mesh, and Knowledge Banks (Vector Stores), or Python-based frameworks where needed.
• Build and orchestrate multi-agent systems using Dataiku's Visual Agents (simple and structured), as well as code-based frameworks (LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK) as appropriate.
• Integrate and optimize LLM APIs across providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure, open-source models via Dataiku's LLM Mesh), applying model routing strategies to balance cost, latency, and quality.
• Implement Retrieval-Augmented Generation (RAG) pipelines, including agentic RAG and GraphRAG, using Dataiku's Knowledge Banks with reranking, dynamic filtering, and document extraction capabilities.
• Work exclusively with the Marketing organisation, partnering across functions such as Demand Generation, Content Marketing, Product Marketing, Field Marketing, Marketing Operations, Brand, and Communications.
• Engage marketing stakeholders to gather business requirements, then go further: identify the underlying user or team pain points those requirements represent, and design solutions that address both the stated need and the deeper problem.
• Own projects end-to-end, from requirements intake and solution design through to build, deployment, and handover.
• Develop autonomous and semi-autonomous AI agents, using Dataiku's Agent Builder, custom Python-based architectures (LangGraph, CrewAI, Claude Agent SDK, etc.), or a combination of both. Exercise judgment on when to leverage platform capabilities and when to build custom solutions.
• Design and build Agent Tools beyond documented examples, including custom API integrations, data retrieval modules, decisioning logic, and automated workflows, pushing past out-of-the-box patterns to deliver solutions tailored to specific business problems.
• Build, publish, and consume MCP (Model Context Protocol) servers to enable agent-to-tool integration across systems, including designing custom MCP servers where needed.
• Develop evaluation and monitoring approaches for agent systems, combining Dataiku's built-in capabilities with custom instrumentation to measure reliability, accuracy, cost, and business impact in production.
• Design and maintain evaluation frameworks (evals) for LLM-based systems, measuring accuracy, latency, cost, and reliability in production.
• Adhere to data governance, security, and regulatory compliance requirements (EU AI Act awareness, responsible AI practices) for all AI solutions.
• Leverage Dataiku's Cost Guard and Quality Guard features to manage LLM spend, enforce usage policies, and maintain output quality standards.
• Work closely with analytics and data engineering teams to maintain metadata on reference datasets for LLM consumption.
• Create front-end user interfaces for AI applications using HTML, CSS, and JavaScript, within Dataiku's webapps framework, Dataiku Answers for chat-based interfaces, or standalone applications built with Vue.js and Node.js.
• Collaborate on UX design, ensuring internal stakeholders find AI solutions intuitive and responsive.
• Provide product feedback to the development team to improve the platform.
• Stay current with the rapidly evolving AI engineering landscape, agent frameworks, model capabilities, evaluation practices, governance requirements, and tools like MCP and A2A protocols.

Generative AI Engineer

Customer Success Manager

• Actively serves a portfolio of assigned accounts including some of the world’s leading organizations in industries such as financial services, insurance, pharmaceuticals, transportation, manufacturing, and technology.
• Gains an understanding of client’s use cases and desired business outcomes and helps a large number of licensed users achieve these goals via Dataiku's Product and Services.
• Helps clients translate the business use cases they’re trying to crack into data science solutions and works with the customers to provide value assessments around the use cases and efficiency improvements.
• Provides guidance to customer organizations on how to leverage Dataiku to implement data science projects from design to production while actively ensuring adoption and growth strategies are in place and being worked.
• Implements customer engagement strategies including consistent Success Reviews, user community events, and customer participation in Dataiku community events to ensure customer engagement and satisfaction.
• Leverages customer health analytics to identify customer expansion opportunities and downsell and churn risks.
• Collaborates with Dataiku’s Sales team to ensure successful renewals and to expand customer relationships.

Strategic Customer Success Manager

• engaging prospects and customers about their related initiatives
• managing the entire sales cycle and own a territory with prospects and customers across a variety of industries
• collaborate cross-functionally with colleagues in marketing, product management, and sales engineering
• showcase the business value of Dataiku’s platform, DSS

Strategic Account Executive – West

Product Marketing

• Own the go-to-market for visual analytics and data preparation capabilities
• Drive launches and grow share in existing markets
• Develop competitive battlecards and support competitive strategy
• Collaborate with PMM and product teams on insights and analysis

Product Marketing Manager

• Own the master messaging architecture and AI Success Formula across every product, persona, and campaign
• Lead end-to-end GTM for Dataiku's most strategic launches: positioning, messaging, sales enablement, analyst briefings, and integrated campaigns
• Own positioning for Dataiku's governance framework and translate evolving regulation (EU AI Act, NIST AI RMF) into sales talk tracks and customer assets
• Lead Gartner, Forrester, and IDC engagement including MQ/Wave briefings and strategic inquiries
• Own the company narrative deck, keynote content, and competitive positioning for high-stakes customer and event conversations

Director, Product Marketing

• Manage the entire sales cycle and own a territory with prospects and customers across a variety of industries
• Engage prospects and customers about their related initiatives to help them develop a more efficient approach, leveraging Dataiku's platform
• Collaborate cross-functionally with colleagues in marketing, product management, and sales engineering
• Showcase the business value of Dataiku’s platform, DSS

Enterprise Account Executive, Latin America

• Design end-to-end AI solutions on Dataiku's platform, leveraging Dataiku Agent Hub, Prompt Studio, LLM Mesh, and Knowledge Banks (Vector Stores), or Python-based frameworks where needed.
• Build and orchestrate multi-agent systems using Dataiku's Visual Agents (simple and structured), as well as code-based frameworks (LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK) as appropriate.
• Integrate and optimize LLM APIs across providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure, open-source models via Dataiku's LLM Mesh), applying model routing strategies to balance cost, latency, and quality.
• Implement Retrieval-Augmented Generation (RAG) pipelines, including agentic RAG and GraphRAG, using Dataiku's Knowledge Banks with reranking, dynamic filtering, and document extraction capabilities.
• Work primarily with the “Revenue” organisation, Sales, BDR, Customer Success, Solutions Engineering, Professional Services, Sales Operations and Marketing (approximately 75% of the role), and apply proven solutions and approaches more broadly across the organisation (approximately 25%).
• Engage stakeholders to gather business requirements, then go further: identify the underlying user pain those requirements represent, and design solutions that address both the stated need and the deeper problem.
• Own projects end-to-end, from requirements intake and solution design through to build, deployment, and handover.
• Develop autonomous and semi-autonomous AI agents, using Dataiku's Agent Builder, custom Python-based architectures (LangGraph, CrewAI, Claude Agent SDK, etc.), or a combination of both. Exercise judgment on when to leverage platform capabilities and when to build custom solutions.
• Design and build Agent Tools beyond documented examples, including custom API integrations, data retrieval modules, decisioning logic, and automated workflows, pushing past out-of-the-box patterns to deliver solutions tailored to specific business problems.
• Build, publish, and consume MCP (Model Context Protocol) servers to enable agent-to-tool integration across systems, including designing custom MCP servers where needed.
• Develop evaluation and monitoring approaches for agent systems, combining Dataiku's built-in capabilities with custom instrumentation to measure reliability, accuracy, cost, and business impact in production.
• Design and maintain evaluation frameworks (evals) for LLM-based systems, measuring accuracy, latency, cost, and reliability in production.
• Adhere to data governance, security, and regulatory compliance requirements (EU AI Act awareness, responsible AI practices) for all AI solutions.
• Leverage Dataiku's Cost Guard and Quality Guard features to manage LLM spend, enforce usage policies, and maintain output quality standards.
• Work closely with analytics and data engineering teams to maintain metadata on reference datasets for LLM consumption.
• Create front-end user interfaces for AI applications using HTML, CSS, and JavaScript, within Dataiku's webapps framework, Dataiku Answers for chat-based interfaces, or standalone applications built with Vue.js and Node.js.
• Collaborate on UX design, ensuring internal stakeholders find AI solutions intuitive and responsive.
• Provide product feedback to the development team to improve the platform.
• Stay current with the rapidly evolving AI engineering landscape, agent frameworks, model capabilities, evaluation practices, governance requirements, and tools like MCP and A2A protocols.