Sr. Marketing Data & GTM Engineer

Konica Minolta Business Solutions, U.S.A., Inc.•Ramsey, NJ

3d•Hybrid

About The Position

Konica Minolta is seeking a Senior Marketing Data & GTM Engineer to turn complex, fragmented data into audience intelligence and activation capabilities that support modern go-to-market execution. The role bridges marketing, analytics engineering, revenue operations, and enterprise technology, translating growth priorities into scalable data products and reliable activation workflows. The position develops account and contact segments, scoring and prioritization models, marketing-ready datasets, and governed semantic layers. It connects behavioral, firmographic, intent, campaign, sales, customer, product, and third-party data so that information can be used across the customer lifecycle. The role is based at the company’s Ramsey, New Jersey headquarters in a hybrid environment, with four days onsite and an option to work remotely on Fridays.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Analytics, Mathematics, Statistics, Marketing, Business, or a related discipline, or an equivalent combination of education and relevant professional experience.
At least 7 years of progressive experience in marketing analytics, analytics engineering, data engineering, revenue operations, business intelligence, marketing operations, or a closely related field.
At least 5 years of hands-on experience using advanced SQL with large, complex enterprise datasets.
Expertise in Salesforce, Looker, SAP, and BigQuery.
Deep understanding of B2B demand generation, account-based marketing, lifecycle marketing, lead management, pipeline development, and sales handoffs.
Advanced ability to build account- and contact-level segments using structured, semi-structured, and relevant unstructured data.
Expert SQL skills and strong data-profiling, transformation, optimization, testing, and validation skills.
Strong knowledge of dimensional modeling, entity relationships, identity resolution, semantic layers, reusable metrics, and governed business definitions.
Practical understanding of ETL/ELT, APIs, orchestration, data pipelines, cloud data platforms, metadata, lineage, and observability.
Experience designing and evaluating interpretable scoring, propensity, prioritization, or predictive models.
Working knowledge of CRM, marketing automation, customer data, digital analytics, advertising, and sales engagement ecosystems.
Ability to evaluate and apply AI, machine learning, predictive analytics, natural language processing, and agent-based automation to go-to-market use cases with appropriate governance, transparency, data quality, and human oversight.
Strong systems thinking and ability to translate ambiguous business needs into scalable technical solutions.
Demonstrated ability to work hands-on with data while leading requirements, solution design, stakeholder alignment, testing, and production delivery.
Excellent communication across marketing, sales, analytics, and technology audiences.
Strong ownership, curiosity, judgment, and ability to manage competing priorities.

Nice To Haves

Experience with Python or R for analysis, feature engineering, automation, or model development.
Experience with dbt, Git, CI/CD, automated testing, data catalogs, observability, or data governance tools.
Relevant certifications in cloud data, analytics engineering, business intelligence, CRM, marketing technology, or data governance.

Responsibilities

Partner with Marketing, Sales, and Data Engineering to define data capabilities for audience strategy, demand generation, lifecycle marketing, personalization, and revenue growth.
Improve the use of datasets across legacy systems, a master data lake, and Salesforce to support business needs.
Design and maintain marketing-ready data models connecting account, contact, lead, engagement, campaign, opportunity, customer, product, digital, and third-party data.
Build and govern semantic layers, reusable metrics, dimensions, business definitions, and documentation for marketing analysis and activation.
Translate business questions into data requirements, logical models, transformation rules, acceptance criteria, and technical delivery plans.
Prototype and productionize reusable datasets, features, and data products that accelerate campaign planning and activation.
Develop scalable segmentation for ideal customer profiles, buying groups, whitespace identification, next-best audiences, personalization, cross-sell, and retention programs.
Create, validate, and govern reusable audience components, eligibility criteria, exclusions, routing rules, and activation logic across marketing automation, CRM, advertising, web, sales outreach, and other go-to-market channels; refine them based on channel outcomes.
Work with IT and data owners on identity resolution and account-contact relationship logic to improve match rates, hierarchy accuracy, deduplication, and activation quality.
Define model inputs, weighting, thresholds, decay, negative signals, exclusions, routing logic, and feedback loops with Marketing and Sales.
Back-test models against business outcomes and monitor lift, calibration, stability, coverage, bias, and adoption.
Create reason codes explaining why an account or contact is prioritized and what action to take next.
Embed scores and decision logic into campaign, automation, lead management, and sales workflows.
Write and optimize advanced SQL to profile, join, transform, test, and analyze large, complex datasets in cloud data environments.
Work with IT and Data Engineering to define and improve pipelines, APIs, batch processes, event streams, and platform integrations for marketing use cases.
Assess internal and external data sources for value, quality, coverage, usability, compliance, and integration effort.
Establish data-quality monitoring, investigate source-system discrepancies, identify root causes and business impact, and coordinate durable fixes.
Use version control, peer review, testing, release documentation, and change management to improve reliability and reproducibility.
Identify, prototype, and operationalize predictive analytics, machine learning, AI, experimentation, and intelligent automation use cases for audience discovery, scoring, personalization, campaign activation, measurement, insight generation, and workflow efficiency.
Own marketing logic, business rules, data-product requirements, acceptance criteria, and activation design while partnering with IT and Data Engineering on enterprise architecture, infrastructure, security, and production operations.
Lead discovery, data mapping, solution design, use-case prioritization, testing, user acceptance, and launch readiness for marketing data initiatives.
Identify dependencies and risks, challenge assumptions constructively, and recommend practical paths from concept to scalable implementation.
Coach marketing and analytics partners on data structure, segmentation logic, measurement design, and responsible use of models and scores.