Senior Marketing Data Analyst

N-iX is a software engineering company with around 2,400 engineers. We were founded in Lviv in 2002, we are headquartered in Malta, and we work with clients across the US, the UK, and the DACH region. We help enterprise technology leaders build and modernize software, data, cloud, and embedded systems, and we position ourselves as a Pragmatic AI Software Engineering partner.

Our go-to-market is inbound-led. Most of our new business starts on our website, and our business development team is small. That means marketing data is not a reporting exercise for us. It is how the company decides where to invest.

Why this role exists

We need one person who owns the numbers behind marketing and turns them into decisions. You will connect website behavior, channel performance, and pipeline outcomes into a single view, explain what is driving change, and recommend what we should do next.

You will work directly with the CMO and present to the N-iX leadership team. The work you produce will shape budget allocation, channel strategy, and the annual marketing plan. You will partner with paid search, organic search and content, ABM and events, and brand and PR leads in the marketing team to set targets and measure results. You will also collaborate with  Sales, Business Development, Finance, and Delivery to align on pipeline definitions and shared metrics

What you will do:

Pipeline and performance analytics

• Own marketing pipeline reporting from first visit to qualified opportunity and closed revenue.
• Analyze performance by channel, campaign, content asset, region, and persona, and identify which activities produce qualified pipeline from our ICP (CTO, CIO, and VP Engineering buyers at companies with 500+ employees).
• Separate pipeline volume from pipeline quality, and track conversion and velocity at each funnel stage.
• Build and maintain the weekly qualified pipeline view used in leadership meetings, with written commentary that explains the movement, not only the figures.
• Run quarterly reviews of channel mix and spend efficiency, and propose where to shift budget.

Web and digital analytics

• Treat the website as our primary revenue channel. Measure and improve how visitors move from landing page to conversion.
• Act as the team's GA4 expert, and keep website tracking accurate as the site and campaigns change.
• Track paid search, organic search, ABM, events, and social performance in a consistent framework so the team can compare channels fairly.
• Design and analyze experiments on pages, forms, and campaigns, and report results with clear confidence levels.

Modeling and forecasting

• Use statistical methods such as segmentation, regression, and predictive modeling to find patterns in large marketing and sales datasets.
• Build pipeline and lead volume forecasts using coverage ratios and cohort-based methods to support quarterly planning.
• Develop and maintain an attribution approach that fits a long, multi-touch B2B buying cycle. Explain the trade-offs between models, such as first-touch and last-touch, so stakeholders understand what the numbers can and cannot tell them.

KPI governance and data quality

• Own the reporting data model that joins CRM, web analytics, and ad platform data in our warehouse.
• Build data quality checks and reconciliation steps that catch anomalies before they reach a dashboard.
• When data is wrong, find the cause at the source and fix it with the system owner, rather than patching the report.
• Work with data engineering, sales operations, and IT to keep tracking, CRM data, and reporting accurate and current.

Storytelling and leadership reporting

• Explain why performance changed, in plain language, for executives, marketers, and sales leaders.
• Build dashboards and visuals that make trends easy to read at a glance.
• Frame every insight against company goals: what it means, what the risk or opportunity is, and what you recommend.

Automation, AI, and self-service

• Use AI tools such as Claude as a default part of how you query, model, analyze, and document, not as an occasional experiment.
• Automate recurring reports so your time goes to analysis, not assembly.
• Build self-service reporting so the marketing team can answer routine performance questions without a custom request.

What you bring

Required:

• 5+ years in professional analytics, including 3+ years in marketing or revenue operations at a B2B SaaS, technology services, or IT consulting company.
• English level - Upper Intermediate or higher
• Proven track record of presenting insights through storytelling to senior leaders and driving decision-making changes.
• Strong SQL and working proficiency in Python or R, with applied statistics experience such as predictive modeling or causal inference.
• Advanced GA4 experience, plus hands-on experience with a CRM (Salesforce preferred) and paid media platforms (Google Ads, LinkedIn Ads).
• Working knowledge of the CRM object model (leads, contacts, accounts, opportunities, campaigns) and how funnel metrics map onto it.
• Fluency in B2B funnel metrics such as MQL to Target Opportunity conversion, pipeline velocity, and ACV, and an understanding of why each one matters.
• Experience building dashboards in a BI tool (Power BI, Looker, Looker Studio, or similar).
• Daily use of AI tools in analytical work, with sound judgment about when to trust and when to check the output.
• Experience with B2B funnels with long sales cycles and multiple stakeholders on the buying side.
• A degree in a quantitative field such as Statistics, Mathematics, Economics, Operations Research, or Data Science.

Nice to have:

• Experience with incrementality testing in B2B, such as holdout or lift tests on paid search and LinkedIn, and an understanding of where methods like media mix modeling do and do not apply at lower data volumes.
• Experience building dimensional data models (star or snowflake schemas) in a cloud data warehouse such as BigQuery or Snowflake.
• Python for automation and data quality scripts.
• Experience with intent data, sales engagement, or conversation intelligence platforms.
• Familiarity with SEO tools (Ahrefs, Google Ads, Google Search Console) and behavior analytics (Microsoft Clarity or similar) 
• Familiarity with marketing automation platforms and how data gets distorted at the point of capture