{"id":3038156,"url":"https://alion.io/job/pmi-manager-marketing-data-analytics","title":"Manager, Marketing Data & Analytics","company":{"id":25274,"name":"PMI","domain":"pmi.ai","url":"https://alion.io/company/pmi","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Dayforce","truth_index":{"grade":"A","score":96,"open_postings":14,"ghost_share":0,"stale_share":0.143,"repost_share":0,"time_to_fill_p50_days":48,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Analytics","role_family":"Analytics","seniority":"staff","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":113000,"max_usd":222000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Cortex","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Looker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-10-21T04:00:00Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-24T23:00:19Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"How will you make a difference to PMI?\nThe Manager, Marketing Data and Analytics owns the business data architecture and metric definitions behind Marketing's numbers, and leads the team that measures Marketing. The role defines how marketing, customer, product, and revenue data should be modeled, decides what each metric means, and ensures those definitions are implemented consistently across Snowflake, BI, and natural-language analytics.\nThe role combines hands-on technical depth with team leadership. The individual is expected to be advanced in SQL, to design analytical data models, and to profile, reconcile, and diagnose data directly, so they can specify what engineering builds and verify that the result represents the business correctly. They are equally expected to lead three to four analysts, set the standards those analysts work to, and develop the team's technical capability.\nData Engineering and the Snowflake team own ingestion, infrastructure, orchestration, transformation pipelines, and semantic view implementation. This role represents Marketing in the design of the data layer, writes the specifications those teams build against, and is accountable for ensuring the resulting data products correctly represent the business.\nWhat you’ll bring to the role:\nBusiness Data Architecture, Metric Definitions, and Context\nOwn Marketing's canonical metric definitions and the business logic the governed semantic layer implements, and arbitrate those definitions when marketing, finance, and reporting platforms disagree.\nSpecify the semantic models for the most complex and cross-domain marketing use cases, and validate what the Snowflake team builds against that specification.\nOwn the business data architecture for Marketing, defining the facts, dimensions, grain, relationships, hierarchies, and conformed dimensions that connect marketing, customer, product, transaction, and engagement data.\nDesign analytical models that answer cross-domain questions spanning acquisition, engagement, conversion, certification, membership, renewal, and revenue.\nWrite advanced SQL to profile data, validate sources, reconcile metrics, build reusable datasets, and investigate complex customer and business questions.\nDetermine what unstructured content, such as business documentation, definitions, and prior analyses, must be retrievable alongside structured metrics for natural-language and agent-based analysis, and maintain source-to-metric mappings and lineage.\nOwn the business questions, expected results, and synonym glossary behind the Verified Query Repository supporting Cortex Analyst and natural-language analytics.\nOwn the context layer that makes Marketing's data interpretable to analysts and AI agents, including entity descriptions, metric documentation, approved analytical uses, and known limitations of each source.\nDiagnose discrepancies across Snowflake, BI, Adobe Analytics, marketing platforms, finance reporting, and operational systems.\nIdentify gaps in source data, identity resolution, event structures, and dimensional consistency before they become downstream reporting issues.\nDefine ownership boundaries between source systems, engineered data products, semantic models, and reporting layers.\nTeam Leadership and Capability\nLead, coach, and develop a team of three to four analysts spanning paid media, customer and lifecycle analytics, and digital analytics.\nSet the Marketing Analytics roadmap, allocate the team against it, and manage intake and prioritization from across Marketing.\nEstablish and enforce analytical standards including SQL quality, peer review, testing, documentation, and reuse.\nReview the team's models, queries, and analyses before they reach stakeholders, and hold the quality bar for what gets published.\nGrow the team's depth in dimensional modeling, data modeling practice, and analytical rigor through direct coaching and review.\nManage performance, hiring, and development planning for the team.\nMarketing Measurement and Performance Analytics\nDefine and govern core marketing KPIs including revenue, conversion, CAC and CPA, ROAS, retention, funnel progression, pacing to plan, and channel efficiency.\nOwn the integrity and narrative of recurring executive performance reporting, including monthly business reviews and QBR analytics, with the team producing the underlying analysis.\nDetermine how platform-reported, attribution-based, financial, and incrementality-based measures reconcile, and establish where each is appropriately used.\nLead attribution, cohort, and incrementality work so it produces decision-grade guidance on marketing investment, building the harder analyses directly.\nOwn predictive modeling for Marketing, including membership churn and renewal propensity, certification lapse and recertification likelihood, and product and cross-sell propensity, alongside segmentation, lifecycle, and cohort analysis across CRM, web, campaign, commerce, certification, and membership data.\nDefine the features, target definitions, validation approach, and performance thresholds for predictive models, determine when a score is reliable enough to act on, and establish how scores are refreshed, monitored for drift, and activated in campaign, lifecycle, and retention programs.\nSet analytical standards for A/B testing, holdouts, geo experiments, and conversion-lift studies within the marketing analytics practice, including hypotheses, test populations, success metrics, and readouts.\nPartner with Product and Marketing to define measurable funnels, customer states, lifecycle stages, and behavioral signals for considered-purchase journeys, and hold a clear distinction between attribution, correlation, prediction, and causal measurement when results are communicated.\nAnalytics Delivery and Enablement\nGovern Marketing's analytical products in enterprise BI and visualization platforms, ensuring definitions are sourced from the semantic layer rather than rebuilt in the tool.\nUse Adobe Analytics to investigate digital behavior, traffic, customer journeys, and conversion performance, and work with data from Adobe Experience Platform, CRM, commerce, and paid media systems.\nApply working knowledge of digital event instrumentation, tagging, identity, and schemas to identify measurement and architecture issues at the source, and establish where AI-assisted tooling is applied to SQL development, analysis, documentation, and testing.\nExpand governed self-service and AI-assisted analytics for Marketing across Snowflake, BI, and natural-language surfaces, and represent Marketing data and context requirements in the Marketing AI reference architecture.\nDefine what Marketing data and context AI agents and natural-language tools are permitted to reach, at what grain, and under what access and accuracy controls, and own the evaluation approach that tests answer accuracy before release and monitors it as data and definitions change.\nCross-Functional Partnership\nServe as Marketing's primary analytical counterpart to Data Engineering, Data Architecture, and Data Governance.\nTranslate business questions into analytical requirements and data-product specifications that engineering can build against.\nPartner with Data Engineering on transformation logic, data contracts, source integration, model design, and data-quality requirements.\nPartner with Performance Marketing, Brand, Digital, Product, Lifecycle, and Regional Marketing on measurement needs and analytical priorities.\nPresent analytical findings, performance trends, and recommendations to senior leadership.\nScope of Role\nLeads a team of three to four analysts covering marketing, customer, and performance analytics, while personally designing analytical data models and validating them. Accountable for Marketing's business data architecture, metric definitions, context layer, predictive models, and executive performance reporting across certification, membership, e-learning, events, and other product lines.\nHands-on ownership concentrates in data model design, metric reconciliation, predictive model validation, evaluation of analytical accuracy, and diagnosis of cross-system discrepancies. Semantic view implementation sits with the Snowflake team, built to this role's specification. Routine reporting, campaign analysis, and dashboard production sit with the team, under this role's review and standards.\nData Engineering, Technology, and the Snowflake team own ingestion, infrastructure, orchestration, platform access, transformation pipelines, and semantic view implementation. Enterprise data governance policy, cataloging, and cross-domain stewardship sit outside this role; the incumbent partners with Data Governance and applies those standards within Marketing.\nRole Qualifications:\n10+ years across marketing analytics, customer analytics, analytics engineering, or related disciplines, including prior leadership of an analytics team.\nDemonstrated experience leading and developing an analytics team while remaining hands-on technically.\nWorking knowledge of ETL and ELT concepts, transformation pipelines, data dependencies, and data-quality controls, with the ability to read, review, and challenge logic developed by engineering teams.\nExperience working within layered, dimensional, or medallion data architectures.\nExperience integrating and analyzing data across CRM, marketing, digital, commerce, and transactional systems.\nDevelopment and governance of analytical products in an enterprise BI platform, preferred.\nCustomer data platform architecture including identity resolution, event schema design, and audience activation.\nMarketing measurement including attribution, experimentation, incrementality, conversion, CAC and CPA, and ROAS, and experience directing or building predictive and propensity models such as churn or retention.\nAdvanced SQL including complex joins, CTEs, window functions, transformations, data profiling, reconciliation, and validation, applied to large-scale datasets.\nWorking hands-on experience with Snowflake or another modern cloud data platform, sufficient to profile, query, validate, and reconcile at scale.\nDemonstrated experience designing business and analytical data models, and specifying them for engineering teams to build against.\nDimensional modeling including facts, dimensions, grain, cardinality, slowly changing dimensions, hierarchies, and conformed dimensions.\nMetric governance, lineage, and reconciliation across multiple enterprise systems.\nStrong analytical storytelling and executive communication.\nFamiliarity with semantic layer and context technologies such as Snowflake Semantic Views, Cortex Analyst, Cortex Search, dbt Semantic Layer, or Looker, preferred.\nDigital analytics using an enterprise web analytics platform, preferred.\ndbt or comparable SQL transformation framework, preferred.\nPython or R for advanced analysis, automation, or modeling, preferred.\nGit and version-controlled analytics development, preferred.\nData testing and observability frameworks, preferred.\nMarketing mix modeling, geo experimentation, conversion lift, causal-inference methods, and machine learning applied to churn, propensity, or lifetime value, preferred.\nUp to 10% if 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