Analytics Engineer - Marketing & Customer Experience

Mirion

23h

About The Position

The Analytics Engineer, Marketing & CX will build the data foundation of Mirion’s Marketing and new Customer Experience organization, transforming raw data from disparate marketing and CX systems into clean, tested, well-documented datasets and models that power self-service analytics and revenue-impacting insights. You will design and maintain the pipelines and data models that integrate marketing platforms (marketing automation, web/digital analytics, advertising, and campaign tools), Forsta (surveys/NPS/CSAT), Microsoft Fabric/Power BI/Databricks (enterprise analytics), Salesforce/Dynamics 365 (CRM), SAP (ERP), support ticketing systems, and other sources into a single source of truth for the customer journey — from first marketing touch through post-sale experience and renewal. Your work will directly enable prioritization, closed-loop actions, campaign and cohort revenue attribution, and executive reporting, giving Mirion a trusted, reusable analytics layer for predictive marketing and customer experience management across its diverse business units and product lines. This is a highly leveraged role for someone who thrives on modeling complex data environments, applying software-engineering discipline to analytics, and turning messy source data into reliable, self-service data products the whole organization can trust.

Requirements

5+ years of experience as an analytics engineer, data engineer, or data analyst in a B2B environment, with a strong record of building data models and pipelines across multiple systems.
Advanced proficiency with data transformation and modeling (dbt or equivalent) and modern data platforms (Microsoft Fabric, Databricks, or similar), plus software-engineering practices for analytics — version control (Git), CI/CD, testing, and documentation; strong Power BI or comparable visualization skills.
Advanced SQL and experience integrating and modeling data from multiple sources — marketing platforms, CRM (Salesforce, Dynamics 365), SAP, and survey platforms (Forsta/Qualtrics/Medallia).
Demonstrated ability to model customer cohorts, segmentation, and marketing/financial attribution — linking campaign and experience metrics to revenue outcomes.
Experience with marketing analytics (campaign performance, funnel/pipeline, lead scoring), text analytics, journey analytics, or predictive modeling in a CX or revenue context.
Excellent storytelling with data: able to distill complex analyses into clear insights and recommendations for non-technical stakeholders.
Comfort working in ambiguous, evolving data environments and building scalable data models and analytical frameworks from the ground up.
Strong collaboration skills and a proven track record partnering with marketing, program managers, and cross-functional teams to turn data into action.
Must be authorized to work in the U.S. without the company’s immigration sponsorship now or in the future. The company will not offer immigration sponsorship for this position.

Nice To Haves

Minimal travel required (~10%), primarily for occasional stakeholder workshops or data alignment sessions.
This role requires access to U.S. export-controlled information. Therefore, employment will be contingent upon the ability to prove that you meet the status of a U.S. Person as one of the following: U.S. lawful permanent resident, U.S. Citizen, have been granted asylee or refugee status (i.e., a protected individual under the Immigration and Naturalization Act, 8 U.S.C. 1324b(a)(3)). The company will not seek an export authorization for this role.

Responsibilities

Design, build, and maintain data pipelines and ELT/transformation workflows that ingest and model data from marketing platforms (e.g., Hubspot, GA4, Intercom, ad and campaign tools), Forsta, Microsoft Fabric, Power BI, Databricks, Salesforce, Dynamics 365, SAP, ticketing systems, and installed-base data into unified, reusable marketing and CX datasets.
Develop and own the transformation layer — dimensional models, a semantic/metrics layer, and curated data marts spanning marketing (campaign performance, funnel/pipeline, lead scoring, attribution), survey data (NPS, CSAT, verbatim text analytics), operational metrics (support tickets, renewal rates, service attachment), and financial outcomes (revenue per account, churn, capital cycles).
Collaborate closely with the CX Operations and Insights Manager and Marketing stakeholders to model customer and account segments, identify at-risk/expansion opportunities, and define prioritized actions for enterprise, business units, and product lines.
Build and maintain the certified datasets, enterprise dashboards, and cohort reports that power marketing and CX analytics in Databricks/ Power BI/Forsta, and enable predictive models (e.g. next-purchase propensity, campaign response, service upsell likelihood).
Model the end-to-end customer journey (marketing touchpoints through post-sale experience) and enable deep-dive analyses — segmentation, driver analysis, and attribution — that quantify the financial impact of marketing programs and experience gaps.
Support closed-loop and marketing-activation processes by providing data-driven targeting (e.g., detractor lists with revenue-at-risk flags, campaign audiences) and modeling action and campaign outcomes.
Automate reporting and data delivery (weekly revenue-at-risk lists, campaign and cohort performance, annual ROI reviews) for CX and Marketing leadership, divisional stakeholders, and Finance.
Apply software-engineering best practices to analytics — version control (Git), CI/CD, automated testing, and documentation — and partner with IT and data teams to improve data quality, hygiene, observability, and integration.
Translate complex data into clear, well-documented, executive-ready datasets and insights — telling the “so what” story and making it easy for others to self-serve.
Stay current on analytics-engineering and marketing-analytics best practices and tools relevant to B2B/industrial marketing and CX (dbt/transformation frameworks, marketing attribution, text analytics, predictive modeling).