Overview

Department: Technology

Responsibilities

• Assemble large, complex sets of data that meet non-functional and functional business requirements
• Identify, design and implement internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
• Build required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS and SQL technologies
• Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition
• Work with stakeholders including data, design, product and executive teams and assisting them with data-related technical issues
• Work with stakeholders including the Executive, Product, Data and Design teams to support their data infrastructure needs while assisting with data-related technical issues
• Generate or adapt equipment and technology to serve user needs
• May perform other job duties as directed by Employee's Leaders
• Build data pipelines that validate digital-asset metadata schemas, content taxonomy, asset performance, and workflow data.
• Use SQL and Python to develop automated validation scripts for metadata tagging and XDM/schema mappings across DAM, AEM, and enterprise data warehouses.
• Build and test ETL pipelines that aggregate asset-utilization, digital-rights-management, and campaign-enrichment data from batch and streaming sources.
• Implement data-quality frameworks that detect metadata drift, missing campaign tags, schema errors, and duplicate asset records.
• Audit asset-tracking models so content velocity, localization variants, and asset-attribution metrics reconcile across Workfront, publishing platforms, and reporting dashboards.
• Support data-quality, governance, and monitoring practices across Content Supply Chain workflows.
• Partner with MarTech, DAM, analytics, and platform teams to resolve data and workflow integration issues.
• Translate data quality findings into actionable improvements for downstream analytics and marketing reporting.
• Knowledge of the practical application of engineering science and technology, including applying principles, techniques, procedures, and equipment to the design and production of various goods and services
• Knowledge of design techniques, tools, and principles involved in production of precision technical plans, blueprints, drawings, and models
• Ability to use logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems
• Ability to understand new information for both current and future problem-solving and decision-making
• Skilled in identifying complex problems and reviewing related information to develop and evaluate options and implement solutions
• Ability to recognize when an issue has occurred or is likely to occur, without needing to diagnose or resolve the problem.
• Ability to combine pieces of information to form general rules or conclusions (includes finding a relationship among seemingly unrelated events)
• Ability to switch efficiently between multiple tasks or information sources, such as spoken instructions, system alerts, or data inputs.
• Ability to organize data or actions in a defined sequence or structure based on specified rules or patterns (e.g., numerical, textual, visual, or mathematical sequences).
• Ability to recognize defined patterns—such as shapes, words, or signals—even when they are embedded within distracting or complex information.
• Advanced SQL and Python skills for data validation, scripting, ETL, and schema quality checks.
• Strong understanding of digital-asset metadata schemas, content taxonomy, DAM data, and workflow data.
• Strong data-quality framework design capability, including drift, completeness, duplicate detection, and schema validation.
• Knowledge, Skills and Abilities

Qualifications

• Ability to audit content and asset-tracking models and reconcile operational metrics.
• Strong analytical and problem-solving skills within MarTech and Content Supply Chain environments.
• Required: Intermediate level experience, fully functioning broad knowledge in:
• Cloud infrastructure, DataLake
• ETL experience ensuring source to target data integrity
• Various filetypes (Delimited Text, Fixed Width, XML, JSON, Parque).
• ServiceBus, setting up ingress and egress within a subscription, or relevant AWS Cloud services administrative experience.
• Unit Testing, Code Quality tools, CI/CD Technologies, Security and Container Technologies
• Agile development experience and Agile ceremonies and practices
• 2-5 years of relevant work-related experience
• Preferred:
• Familiarity with Databricks.
• Working knowledge of Customer Data Platforms (CDPs).
• AWS monitoring experience.
• CI/CD security and compliance exposure.
• Python/SQL scripting depth.
• AEM exposure is a nice to have; MarTech domain knowledge is required.
• Must meet confidentiality expectations as to confidential, proprietary and sensitive Company information
• Ability to work extended hours as needed
• Ability to work onsite approximately 3 days per week and as required by the business.
• Turn Content Supply Chain data into reliable marketing and business insights.
• Improve asset utilization, content-quality measurement, and data integrity across enterprise touchpoints.
• Build data-quality foundations that help leadership measure efficiency, ROI, and content-driven outcomes.

Education

• Required: Bachelor's degree in Computer Science, Engineering, Information Systems or related field and/or equivalent formal training