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PMI
PMI
EducațieNonprofit
PMI, Institutul pentru Managementul Proiectelor, este o asociație profesională globală non-profit axată pe educație și dezvoltare profesională în managementul proiectelor și programelor. Organizația elaborează standarde și ghiduri pentru industrie, inclusiv Ghidul PMBOK, și oferă certificări precum PMP, CAPM, PMI-ACP și PMI-CPMAI. PMI oferă, de asemenea, cursuri de formare, evenimente, cercetare, leadership de opinie, instrumente digitale precum PMI Infinity, soluții pentru întreprinderi și servicii de membru pentru profesioniștii din managementul proiectelor.
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Marketing Data Analytics Manager

Leads PMI’s marketing data analytics, overseeing data architecture, metric governance, predictive modeling, and executive reporting. Manages analysts and partners with engineering to build trusted marketing data products.

USStatele Unite ale AmericiiRemoteNormă întreagăSeniorLiderLimbă Obligatorie: englezăAnalist de date
CloudETLPythonSQL

Descriere

  • Define Marketing’s core metrics and business rules for its governed semantic layer
  • Specify semantic models for complex marketing needs across domains, and review Snowflake implementations
  • Own Marketing’s data architecture, including facts, dimensions, grain, relationships, hierarchies, and conformed dimensions
  • Build analytical models covering acquisition, engagement, conversion, certification, membership, renewal, and revenue
  • Use advanced SQL to profile data, validate sources, reconcile metrics, create reusable datasets, and answer complex questions
  • Maintain source-to-metric mappings, lineage, documentation, entity descriptions, approved uses, and known data limitations
  • Manage the business questions, expected results, and synonym glossary in the Verified Query Repository for Cortex Analyst and natural-language analytics
  • Investigate discrepancies across Snowflake, BI, Adobe Analytics, marketing platforms, finance reports, and operational systems
  • Find gaps in source data, identity resolution, event structure, and dimensional consistency
  • Set clear ownership across source systems, engineered data products, semantic models, and reporting layers
  • Lead, coach, and develop three to four analysts
  • Set the Marketing Analytics roadmap, assign resources, and oversee work intake and prioritization
  • Set standards for SQL quality, peer review, testing, documentation, reuse, modeling, and analytical rigor
  • Review models, queries, and analyses before they are shared with stakeholders
  • Oversee team performance, hiring, and development planning
  • Define and govern marketing KPIs such as revenue, conversion, CAC, CPA, ROAS, retention, funnel progression, pacing, and channel efficiency
  • Ensure the accuracy and clarity of recurring executive reports, including monthly business reviews and QBR analytics
  • Reconcile measures reported by platforms, attribution models, finance, and incrementality studies
  • Lead attribution, cohort, incrementality, predictive modeling, segmentation, lifecycle, and retention analyses
  • Set predictive-model features, targets, validation methods, performance thresholds, refresh schedules, drift monitoring, and activation controls
  • Set standards for A/B tests, holdouts, geographic experiments, and conversion-lift studies
  • Work with Product and Marketing on funnels, customer states, lifecycle stages, and behavioral signals
  • Govern Marketing analytics products across enterprise BI and visualization platforms
  • Use Adobe Analytics and data from Adobe Experience Platform, CRM, commerce, and paid media systems to analyze digital behavior and conversion
  • Apply expertise in digital instrumentation, tagging, identity, and schemas to find measurement and architecture issues
  • Grow governed self-service and AI-assisted analytics across Snowflake, BI, and natural-language tools
  • Set access, data-grain, accuracy, and evaluation controls for AI agents and natural-language tools
  • Represent Marketing in analytical work with Data Engineering, Data Architecture, and Data Governance
  • Turn business questions into analytical requirements and data-product specifications
  • Work with engineering on transformation logic, data contracts, source integration, model design, and data quality
  • Partner with Marketing teams on measurement needs and priorities
  • Present findings, performance trends, and recommendations to senior leaders

Cerințe

  • At least 10 years in marketing analytics, customer analytics, analytics engineering, or a related field, including prior leadership of an analytics team
  • Experience leading and developing an analytics team while maintaining hands-on technical skills
  • Working knowledge of ETL and ELT concepts, transformation pipelines, data dependencies, and data-quality controls
  • Experience with layered, dimensional, or medallion data architectures
  • Experience integrating and analyzing data from CRM, marketing, digital, commerce, and transactional systems
  • Customer data platform architecture experience, including identity resolution, event schema design, and audience activation
  • Marketing measurement experience spanning attribution, experimentation, incrementality, conversion, CAC and CPA, and ROAS
  • Experience directing or building predictive and propensity models, such as churn or retention models
  • Advanced SQL skills, including complex joins, CTEs, window functions, transformations, data profiling, reconciliation, and validation on large datasets
  • Hands-on experience with Snowflake or another modern cloud data platform
  • Experience designing business and analytical data models and specifying them for engineering teams
  • Dimensional modeling knowledge, including facts, dimensions, grain, cardinality, slowly changing dimensions, hierarchies, and conformed dimensions
  • Experience governing metrics, lineage, and reconciliation across multiple enterprise systems
  • Strong analytical storytelling and executive communication skills
  • Familiarity with semantic-layer and context tools such as Snowflake Semantic Views, Cortex Analyst, Cortex Search, dbt Semantic Layer, or Looker is preferred
  • Experience with an enterprise web analytics platform is preferred
  • Experience with dbt or a comparable SQL transformation framework is preferred
  • Experience using Python or R for advanced analysis, automation, or modeling is preferred
  • Experience developing analytics with Git and version control is preferred
  • Experience with data testing and observability frameworks is preferred
  • Knowledge of marketing mix modeling, geographic experimentation, conversion lift, causal inference, or machine learning for churn, propensity, or lifetime value is preferred
  • Willingness to travel up to 10% if required

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