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Senior Director, Marketing Operations & Analytics

AI Summary
Dataiku is seeking a senior marketing operations and analytics leader to report to the CMO and manage a global team of four. The role owns marketing pipeline targets, attribution, performance measurement, campaign operations, marketing technology, and investment analysis in partnership with Sales. It also leads the design, governance, adoption, and measurement of AI agents and GenAI workflows for marketing teams. The position requires enterprise B2B software experience, strong executive influence, and hands-on expertise with platforms such as Salesforce, HubSpot, 6sense, Snowflake, and related tools.
Remote from
USA
Salary
Undisclosed
Employment
Full Time
Experience
Director
Published
Apply before
4 Dec 2026
Listing views
22
Application actions
2
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Opportunity details

About this role.

Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams design and operate analytics, machine learning, and AI agents with the transparency, collaboration, and control enterprises require. Sitting above data platforms, cloud infrastructure, and AI services, Dataiku connects the full enterprise AI stack – empowering organizations to run AI across multi-vendor environments with centralized governance.

The world’s leading companies rely on Dataiku to operationalize AI and run it as a true business performance engine delivering measurable value. For more, visit the Dataiku blog, LinkedIn, X, and YouTube.

About the role

Sr. Director | Reports to the CMO | Leads a team of 4 | Global scope across the Americas, EMEA, and APJ | Location: US

You will lead the team that sets marketing’s pipeline targets with Sales, measures what drives revenue, runs campaign operations, and builds the AI agents our marketers use every day. You will bring a point of view to the marketing leadership team on where we invest.

Why this role

  • Own the pipeline number with Sales. You will set marketing’s pipeline targets by region, segment, and channel, and measure progress against them.
  • Build an AI-run marketing team on Dataiku. You will design and govern the agents our marketers rely on, and your work becomes a story we tell customers.
  • Shape where the money goes. Your analysis guides marketing investment decisions and reaches the CMO and the board.
  • High leverage, global reach. A team of four covering three regions, with a mandate to grow capacity through automation and AI rather than headcount.

What you will do

Set and measure the number

  • Build the funnel model that works back from bookings and ARR targets to pipeline, engagement, and coverage targets, agreed with Sales.
  • Launch a new account-based attribution model and make it the one Sales uses in pipeline reviews.
  • Own the marketing performance scorecard: one source of truth, shared with Sales, from brand awareness through account engagement to pipeline.
  • Measure the return on every program and event, so we fund what works and cut what doesn’t.

Run the marketing engine

  • Lead campaign operations in HubSpot and Salesforce, with clear intake, quality, and turnaround standards for global and regional teams.
  • Own the marketing technology stack, roadmap, and budget, and simplify it.
  • Build account scoring, segmentation, and routing that match how Sales covers accounts.
  • Set the lifecycle, campaign naming, and data standards that make reporting trustworthy.

Build the AI layer

  • Build and govern Marketing’s AI agents and GenAI workflows on Dataiku, from first idea through evaluation, review, and monitoring.
  • Get every marketer using these tools in their weekly work, and report the productivity gains.
  • Use automation and agents to grow the team’s output without growing its size.

Guide investment

  • Provide the spend and performance data behind marketing’s planning and budget decisions.
  • Run experiments and recommend where to move budget each quarter, by channel, region, and program.
  • Make performance data self-service, so marketers can answer their own questions.
  • Own data quality, privacy and consent compliance (including GDPR), and responsible AI use across marketing.

What you bring

  • 10+ years in marketing operations, analytics, or revenue operations at an enterprise B2B software company, including 4+ years leading teams.
  • An attribution model you shipped that Sales trusted and used.
  • Account-based measurement built with Sales, including target account engagement and buying-group progression.
  • A hands-on background (attribution, and AI agents or GenAI workflows in production) and the experience to now lead through a team, staying close enough to the work to unblock it.
  • Experience with Salesforce, HubSpot, 6sense, Clay, Gong, Snowflake, or similar tools. Dataiku experience is a plus.
  • Experience working across regions with different levels of data and process maturity.

How you work

  • You think like a business leader. You use data to shape strategy and investment, and you bring a point of view.
  • You treat pipeline as a number shared with Sales, and you agree on definitions before you build dashboards.
  • You influence senior stakeholders with data. You challenge plans the numbers don’t support and defend the ones they do.
  • You are credible with engineers and with regional marketers.
  • You see governance as what lets a team move fast safely.

#LI-CM

What are you waiting for!

At Dataiku, you’ll be part of a journey to shape the ever-evolving world of AI. We’re not just building a product; we’re crafting the future of AI. If you’re ready to make a significant impact in a company that values innovation, collaboration, and your personal growth, we can’t wait to welcome you to Dataiku! And if you’d like to learn even more about working here, you can visit our Dataiku LinkedIn page.

Our practices are rooted in the idea that everyone should be treated with dignity, decency and fairness. Dataiku also believes that a diverse identity is a source of strength and allows us to optimize across the many dimensions that are needed for our success. Therefore, we are proud to be an equal opportunity employer. All employment practices are based on business needs, without regard to race, ethnicity, gender identity or expression, sexual orientation, religion, age, neurodiversity, disability status, citizenship, veteran status or any other aspect which makes an individual unique or protected by laws and regulations in the locations where we operate. This applies to all policies and procedures related to recruitment and hiring, compensation, benefits, performance, promotion and termination and all other conditions and terms of employment. If you need assistance or an accommodation, please contact us at: reasonable-accommodations@dataiku.com


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Dataiku will never ask you for payment of any type during the interview or hiring process. Other than our video-conference application, Zoom, we will also never ask you to make purchases or download third-party applications during the process. If you experience something out of the ordinary or suspect fraudulent activity, please review our page on identifying and reporting fraudulent activity here.

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a high-impact director role accountable for global pipeline performance, marketing investment decisions, data governance, and AI transformation. Success requires strategic leadership while remaining hands-on with attribution, operations, analytics, and cross-functional alignment with Sales and executive stakeholders.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$250,000
US market range (per year)$220k–$280k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The yearly cadence and USD market estimate are inferred for a US-based, full-time Senior Director of Marketing Operations & Analytics role at an enterprise B2B software company.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you build a funnel model that connects bookings and ARR targets to marketing pipeline targets?

I would begin with agreed definitions for bookings, qualified pipeline, stages, conversion rates, sales-cycle length, and marketing-sourced or influenced credit. I would model required pipeline backwards from regional and segment-level bookings goals using historical conversion and velocity data, then set coverage and engagement targets by channel. I would review assumptions with Sales leadership regularly and update the model as performance changes.

Describe how you would launch an account-based attribution model that Sales will trust.

I would involve Sales and RevOps early to define the target-account hierarchy, buying groups, meaningful engagement, opportunity stages, and attribution use cases. I would launch with transparent logic and a limited pilot, reconcile results against known deals, and clearly distinguish correlation from contribution. Adoption would be supported through pipeline-review workflows, accessible dashboards, and a feedback loop for improving the model.

What approach would you use to govern AI agents and GenAI workflows in marketing?

I would establish a lifecycle covering intake, business-case definition, data-access controls, testing, human review, deployment, monitoring, and periodic reassessment. Each agent would have an accountable owner, documented inputs and outputs, quality metrics, and defined escalation paths. I would also ensure privacy, consent, brand, and responsible-AI requirements are built into the workflow rather than added after launch.

How do you decide where to reallocate marketing budget during a quarter?

I evaluate spend alongside pipeline creation, conversion quality, opportunity velocity, incremental impact, and payback by channel, region, segment, and program. I account for data maturity and attribution limitations, then pair dashboard findings with controlled experiments where possible. I present recommendations with clear trade-offs, confidence levels, and the expected effect on pipeline coverage and bookings.

How would you improve data quality across global marketing teams with different process maturity levels?

I would define a small set of global non-negotiable standards for lifecycle stages, campaign naming, consent, account matching, and core reporting fields. Regional teams would receive practical templates, validation rules, service-level expectations, and enablement tailored to their workflows. Shared scorecards and regular data-quality reviews would make ownership visible while allowing justified local flexibility.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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