Dataiku is looking for a Data Engineer to join our Enterprise Data and Analytics (EDA) team. As a member of the EDA team, you will play a central role in delivering data to fuel analytics and data-driven insights to various stakeholders and teams within the company. You will also be a key technical member contributing to the data platform that fuels centralized analytics, embedded analytics teams, Generative AI engineering, and self-service users across the organization.

This role is about 50% Data Operations, Support & Troubleshooting, and 50% new development. The data engineering day-to-day will primarily be within the data platform built using Snowflake, Dataiku, and GitHub. Primary development will focus on Python & SQL, DataOps processes built within GitHub Actions & Dataiku, and data platform processes built within Snowflake & Dataiku.

Non-technical skills and learning are also critical, as you will collaborate with engineers from various teams and help deliver solutions across a wide variety of technical domains. The ideal candidate is naturally curious, has excellent verbal and written communication skills, a sharp analytical mind, a positive attitude towards work, and thrives when collaborating towards a shared goal.

This is an internal and non-client facing role.

What you’ll do: 

Dataiku is unique in that every Dataiker is encouraged to use our own product within our Enterprise Data Platform.  That means this is a unique opportunity to deliver a scalable platform with governed data to fuel an entire company of current or potential Data Analysts & Data Consumers! Your responsibilities within the team include but are not limited to:

• Develop engineering expertise within the Dataiku Platform to help maintain and develop system integrations, platform automations, and platform configurations.

• Develop engineering expertise  within Snowflake for data engineering and security/governance features

• Build & maintain python & SQL data replication & data pipelines on large & often complex data sets 

• Build & maintain data quality metrics & observability to help drive data quality standards

• Learn about existing systems and  processes across Data Platforms, Data Engineering and Data Governance

• Troubleshoot data pipelines, platform automations, data access system.

• Help field and troubleshoot various community questions and challenges

• Own, maintain and enhance data operation processes, monitoring & data quality systems

• Design data models for both short term and long term use cases to support data warehouse scalability

• Build & maintain administration systems and applications for monitoring, alerting, data observability, access management, platform metrics, and end user transparency

• Identify opportunities for improvements & optimization for greater scalability & delivery velocity

• Collaborate closely with Analytics Engineers to provide data & data models for analytical deliverables

• Perform root cause analysis on often complex errors to help ensure data pipeline availability

• Help test new features in Dataiku and partner tools to both provide feedback internally as well as determine value towards internal analytics & data platform integration

• Work closely with key stakeholders across the organization including Infra, embedded analytics teams, Product and Engineering to help foster both technical implementations & requirements gathering

• Proactively drive innovation internally with bringing ideas for platform and process improvements 

• Help contribute to the ongoing documentation of internal systems and processes

Requirements: 

• 2+ years of relevant experience in Data Engineering / Data Platform Engineering

• Strong technical skills in SQL & Python are a must. Experience in Dataiku DSS is a big plus. 

• Prior experience with Snowflake a plus

• Prior experience with DevOps technologies such as Github Actions, Azure DevOps or Jenkins

• Experience in building data models

• Prior experience building and maintaining replication & data pipelines in a cloud data warehouse or data lake environment

• Excellent analytical and creative problem-solving skills - exhibit confidence to ask questions to bring clarity, share ideas, and challenge the norm.

• Passion for continuous learning and teaching to help learn & teach new technologies & implementation strategies

• Experience working with complex stakeholders; dissecting vague asks and helping to define tangible requirements

• Ability to manage multiple projects and time constraints simultaneously in a high-trust remote environment

• Ability to wear multiple hats depending on the project with the focus on accomplishing end goals while inspiring colleagues to do the same

• Excellent written and verbal communication skills (especially with senior-level stakeholders) with the ability to speak to both the business value, data products, & technical capabilities of a platform. Ability to create clear and concise documentations with a high degree of precision

Dataiku is looking for a Data Engineer II to join our Enterprise Data and Analytics (EDA) team. As a member of the EDA Team, you will play a central role in delivering data to fuel analytics, AI, and data-driven insights to various stakeholders and teams within the company. You will also be a key technical member contributing to the Data Platform that fuels centralized analytics, Generative AI engineering, embedded analytics teams, and self-service users across the organization.

You will become a technical expert on the various platforms we work in and help drive engineering excellence both within the EDA team and across the wider Analytics Community. The Data Engineering day to day will primarily be within the Data Platform built using Snowflake, Dataiku, and GitHub. Primary development will focus on Python & SQL, DataOps processes built within GitHub Actions & Dataiku, and data platform processes built within Snowflake & Dataiku.

Non-technical skills and learning are also critical, as you will collaborate with engineers from various teams and help deliver solutions across a wide variety of technical domains. Strong software development lifecycle knowledge and DataOps skills are a must. The ideal candidate is naturally curious, has excellent verbal and written communication skills, a sharp analytical mind, a positive attitude towards work, and thrives when collaborating towards a shared goal.

This is an internal and non-client-facing role.

What you’ll do: 

Dataiku is unique in that every Dataiker is encouraged to use our own product within our Enterprise Data Platform.  That means this is a unique opportunity to deliver a scalable platform with governed data to fuel an entire company of current or potential Data Analysts! Your responsibilities within the team include but are not limited to:

• Be an expert level engineer within the Dataiku Platform including Platform Automation, GenAI Capabilities, Plugin Development, maintenance & troubleshooting

• Be an expert  level engineer within Snowflake for data engineering and security/governance features

• Build & maintain python & SQL based platform automation process

• Build & maintain data quality metrics & observability to help drive data quality standards

• Design data models for both short term and long term use cases to support data warehouse scalability

• Build & maintain administration systems and applications for monitoring, alerting, data observability, access management, platform metrics, and end user transparency

• Build & maintain GenAI Platform platform solutions focused on security and governance for engineering delivery

• Build & maintain DataOps process for SDLC delivery

• Identify opportunities for improvements & optimization for greater scalability & delivery velocity

• Collaborate closely with Analytics Engineers to provide data & data models for analytical deliverables

• Perform root cause analysis on often complex errors to help ensure data pipeline availability

• Help drive technical & architectural decisions on the data platform including decisions on data architecture, data engineering processes, data quality frameworks, data access security & governance frameworks, DataOps processes & data consumption models.

• Help test new features in Dataiku and partner tools to both provide feedback internally as well as determine value towards internal analytics & data platform integration

• Work closely with key stakeholders across the organization including Infra, embedded analytics teams, Product and Engineering to help foster both technical implementations & requirements gathering

• Proactively drive innovation internally with dedicated innovation time & projects that aim to be transformational for either the platform, team or company as a whole.

• Actively contribute to the expertise level and competencies of the EDA Team and participate in the creation and support of data development standards and best practices. 

Requirements : 

• 3+ years of relevant experience in Data Engineering / Data Platform Engineering

• Expertise in SQL & Python is a must. Experience in Dataiku DSS is a big plus. 

• Prior experience with Snowflake strongly desired

• Prior experience with DevOps technologies such as Github Actions, Azure DevOps or Jenkins

• Strong understanding of data architecture & data modeling concepts

• Prior experience building and maintaining replication & data pipelines in a cloud data warehouse or data lake environment

• Excellent analytical and creative problem-solving skills - exhibit confidence to ask questions to bring clarity, share ideas and challenge the norm.

• Passion for continuous learning and teaching to help learn & teach new technologies & implementation strategies

• Experience working with complex stakeholders; dissecting vague asks and helping to define tangible requirements

• Ability to manage multiple projects and time constraints simultaneously in a high trust remote environment

• Ability to wear multiple hats depending on the project with the focus on accomplishing end goals while inspiring colleagues to do the same

• Excellent written and verbal communication skills (especially with senior level stakeholders) with the ability to speak to both the business value, data products, & technical capabilities of a platform.  Ability to create clear and concise documentations with a high degree of precision

As a Sr Generative AI Engineer on the ED&A team, you will build the agentic AI systems that change how Dataiku runs internally. The role is hands-on and end-to-end, you'll work close to the business, turn real problems into working software, and see your solutions through from first conversation to production.

How You'll Make an Impact

Agentic AI Solution Development & Integration

• 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.

Stakeholder Engagement & Delivery

• 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.

Agent & Tool Development

• 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.

AI Governance & Evaluation

• 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.

Web Application Development

• 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.

Continuous Learning

• 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.

What You'll Need to Be Successful

Technical Proficiency

• Must have strong Python skills (including familiarity with typical data science and AI engineering libraries).

• Must have hands-on experience building agentic AI systems, multi-agent orchestration, tool chaining, autonomous decision-making, and production deployment of AI agents.

• Experience with modern agent orchestration frameworks (LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK, or similar); familiarity with LangChain is still relevant but not sufficient on its own.

• Understanding of RAG architectures (vector databases, embedding strategies, agentic RAG, GraphRAG) and when to apply each approach.

• Familiarity with MCP (Model Context Protocol) for agent-to-tool integration, or demonstrated ability to quickly adopt new integration standards.

• Experience with structured outputs, function/tool calling, and prompt engineering across multiple LLM providers.

• Web development fundamentals (HTML, CSS, JavaScript); experience with Vue.js and Node.js preferred. 

• Exposure to AI evaluation practices, building evals, monitoring model/agent performance in production, and iterating based on metrics.

• Comfort with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code, or similar).

• Familiarity with Dataiku a bonus.

Educational & Professional Background

• Bachelor's or Master's in Computer Science, Data Science, Engineering, or a related field; equivalent experience also considered.

• Demonstrated ability to integrate multiple technologies, optimize workflows, and deliver user-friendly AI solutions in a production setting.

Soft Skills

• Strong communication and presentation skills, capable of collaborating effectively with both technical and non-technical stakeholders.

• Problem-solving mindset with a passion for innovation and delivering measurable business value.

• Openness to learning new tools (e.g., Dataiku) and adapting to a rapidly evolving AI landscape.