Marketing Analytics Engineer, Data Products & Activation

Zelis • St. Louis, MO

2d • $127,000 - $160,550 • Hybrid

About The Position

Zelis is seeking a Marketing Analytics Engineer, Data Products & Activation to help build the next generation of marketing data, AI, and activation capabilities. This role will translate marketing, website, sales, CRM, paid media, and engagement signals into governed marketing data products that support audience intelligence, journey analytics, segmentation, personalization, lead qualification, attribution, sales activation, DXP experiences, and AI-enabled decisioning. The role will help mature Snowflake from campaign-specific tables and manual activation logic into reusable, trusted, and activation-ready data products, including Golden Account, Golden Contact, Segment Membership, Product Interest / Intent, Persona, Buying Committee Role, Consent / Suppression Eligibility, Engagement Score, Intent Intensity, Opportunity Context, Product Ownership, White-Space Opportunity, and Next-Best-Product Candidate. The ideal candidate combines analytics engineering, marketing technology fluency, web data architecture, data product discipline, and AI context-layer design.

Requirements

• 6+ years of experience in analytics engineering, data engineering, marketing analytics engineering, marketing technology, digital analytics, RevOps analytics, or a related technical analytics role.
• Advanced SQL experience and demonstrated ability to build reliable, reusable data models in Snowflake or comparable cloud data warehouse environments.
• Experience translating business requirements into production-quality data products, including source-to-target mapping, transformation logic, QA routines, data contracts, metric definitions, documentation, lineage, and stakeholder enablement.
• Strong understanding of B2B marketing data, including campaign metadata, website behavior, audience segmentation, lead lifecycle, form conversion, CRM outcomes, paid media signals, sales funnel data, opportunity context, buying committees, product interest, and expansion motions.
• Experience with website data architecture, including event design, event taxonomy, event streaming, session/user behavior, conversion tracking, tag governance, and downstream use of digital signals for audience or journey analytics.
• Ability to design and steward canonical marketing data objects such as Golden Account, Golden Contact, Segment Membership, Product Interest / Intent, Persona, Buying Committee Role, Consent / Suppression Eligibility, Engagement Score, Intent Intensity, Opportunity Context, Product Ownership, and White-Space Opportunity.
• Ability to design AI-ready data and context layers that support governed use of LLMs, AI assistants, and agentic workflows across marketing operations, analytics, audience activation, and experience delivery.
• Working knowledge of Salesforce CRM and marketing automation data structures, including leads, contacts, accounts, campaigns, opportunities, campaign membership, lifecycle status, activities, account-contact relationships, and sales engagement signals.
• Experience building data quality controls, automated validation checks, reconciliation routines, monitoring processes, issue triage workflows, and runbooks for recurring marketing data products.
• Ability to partner with marketing, data, sales operations, revenue operations, digital experience, legal/compliance, and technology stakeholders to turn ambiguous use cases into scalable data architecture and clear documentation.

Nice To Haves

• Experience supporting audience activation, reverse ETL, or data syncs from Snowflake into marketing automation, CRM, paid media, digital experience, journey orchestration, or sales activation destinations.
• Experience designing data models for personalization, journey orchestration, next-best-action, lead scoring, propensity scoring, product interest classification, buyer-group intelligence, or expansion/white-space opportunity identification.
• Familiarity with identity resolution concepts, including deterministic matching, probabilistic matching, account/contact hierarchy, domain mapping, anonymous-to-known stitching, survivorship logic, exception queues, and golden record inputs.
• Experience with web analytics instrumentation, tag governance, server-side event collection, event streaming into a cloud data warehouse, or first-party behavioral data activation.
• Experience modeling consent, suppression, communication eligibility, channel eligibility, opt-out source, suppression reason, consent source, consent timestamp, and activation eligibility rules.
• Familiarity with Python, dbt, warehouse-native scripting, version control, CI/CD, automated testing, API-based data movement, QA automation, monitoring, and workflow automation.
• Experience building or maintaining semantic layers, business metric layers, data catalogs, data contracts, product documentation, lineage tooling, observability tooling, or AI-readable metadata frameworks.
• Familiarity with LLM prompting, retrieval-augmented generation, agent workflow design, AI governance patterns, confidence scoring, audit logging, or AI-assisted analytics development.

Responsibilities

• Design, build, and maintain analytics-ready and activation-ready marketing data products in Snowflake that support digital marketing, website engagement, audience segmentation, journey progression, lead qualification, attribution, personalization, DXP experiences, and sales activation.
• Co-own the marketing data product lifecycle, including definitions, source systems, transformation logic, data contracts, refresh cadence, quality thresholds, lineage, downstream consumers, activation destinations, change control, and known limitations.
• Help define and maintain the canonical marketing data model across Snowflake, HubSpot, Salesforce, DXP, paid media, reporting, and activation platforms, including shared definitions for MQL, lifecycle stage, product interest, persona, buying committee role, consent status, suppression eligibility, engagement, and intent.
• Convert one-off audience requests and campaign list logic into reusable segment data products with documented purpose, inclusion logic, exclusion logic, suppression rules, QA checks, refresh cadence, activation destinations, business owner, technical owner, and success metrics.
• Build and manage governed AI context layers that make marketing data usable by LLMs, AI assistants, and agentic workflows, including curated metric definitions, audience attributes, content taxonomy, campaign metadata, web behavior signals, journey states, data lineage, approved business rules, confidence scores, and review thresholds.
• Develop data structures that connect website behavior to known and unknown audience profiles, including page views, content engagement, form interactions, conversion events, source attribution, session behavior, identity resolution inputs, and downstream Salesforce CRM outcomes.
• Partner with Digital Experience, Marketing Automation, Paid Media, Sales Operations, Revenue Operations, Legal / Compliance, Analytics, and Enterprise Data teams to define event-streaming, governance, and activation requirements from Zelis.com and related digital properties into Snowflake.
• Create durable data models that support identity resolution, golden account/contact attributes, engagement scoring, buyer-group intelligence, product interest history, lifecycle movement, product ownership, opportunity context, consent status, suppression logic, and channel eligibility rules.
• Build reusable activation datasets and reverse ETL requirements that send trusted, explainable, consent-safe data from Snowflake into downstream marketing destinations, including email automation, CRM, paid media, digital experience, and journey orchestration platforms.
• Establish data quality checks, reconciliation logic, monitoring routines, product SLAs, and exception handling for marketing data products, with emphasis on website events, campaign metadata, lead lifecycle data, audience membership, consent / suppression eligibility, identity confidence, and activation outputs.
• Support predictive lead qualification, intent scoring, attribution, MQL value, expansion opportunity, and next-best-action work by building clean feature tables, training-ready datasets, model output tables, and explainability-ready structures.
• Use AI-assisted development and analytics techniques to accelerate SQL generation, documentation, data testing, anomaly detection, metadata tagging, and first-pass data product design, while maintaining human review, governance, and quality standards.

Benefits

• 401k plan with employer match
• flexible paid time off
• holidays
• parental leaves
• life and disability insurance
• health benefits including medical, dental, vision, and prescription drug coverage

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed