Data Engineer

Company Overview

World Emblem International is a global manufacturer of patches, emblems, and decorated products. The business operates across ecommerce, sales, finance, production, fulfillment, and marketing systems. As the company expands its AI and internal software initiatives, it needs a reliable data foundation that connects these systems while preserving the purpose and ownership of each operational platform.

Role Summary

The Senior Data Engineer / Data Architect will be the hands-on technical owner of World Emblem's enterprise data foundation. This person will assess the current data environment, define the future architecture, and build the pipelines, models, controls, and data services required to make company data accurate, secure, and useful.

This is not an architecture-only advisory role. The successful candidate must be able to design the target state and personally build the core data pipelines, models, tests, and services needed to deliver it.

Why This Role Exists

Critical business data currently lives across Microsoft Dynamics 365 Business Central, HubSpot, Optimizely, BigCommerce, marketing platforms, production systems, internal servers, and other applications. Using multiple systems is normal. The gap is dedicated ownership for the data that moves between them.

Without a clear cross-system data owner, individual integrations can create duplicate records, conflicting definitions, incomplete reporting, security risks, and growing technical debt. These risks become more important as World Emblem builds AI agents, analytics products, and internal MicroSaaS applications that depend on trusted data.

Key Responsibilities1. Data Audit and Current-State Mapping

• Create and maintain an inventory of data sources, databases, APIs, integrations, scheduled jobs, reports, owners, and downstream users.

• Map how customer, product, order, revenue, inventory, marketing, and production data currently moves across the company.

• Identify duplicate data, missing ownership, weak controls, manual work, reconciliation gaps, security risks, and fragile integrations.

• Document the current architecture and establish a clear baseline for future improvements.

2. Enterprise Data Architecture

• Define the authoritative system for each major data domain and, where necessary, for specific fields within that domain.

• Design a scalable target architecture that supports operational systems, reporting, AI, and internal applications without turning one business platform into the data platform for the entire company.

• Create common data models and identifiers for customers, companies, products, orders, revenue, inventory, locations, and production activity.

• Set standards for batch processing, real-time events, APIs, data contracts, schema changes, and data retention.

• Recommend the right data platform and integration tools based on business needs, security, cost, maintainability, and the existing technology environment.

3. Data Engineering and Integration

• Build and maintain reliable data pipelines connecting Business Central, HubSpot, ecommerce platforms, marketing platforms, production systems, and internal applications.

• Develop tested transformations that turn source data into consistent, reusable business data.

• Create secure APIs and data services that allow approved analytics, AI, and internal tools to use trusted data.

• Use source control, automated testing, deployment pipelines, and clear release practices for data code and configuration.

• Design integrations that can recover from failures, handle changing schemas, and avoid duplicate processing.

4. Data Quality and Reliability

• Create automated checks for completeness, accuracy, duplication, freshness, and consistency.

• Reconcile key measures such as orders, revenue, inventory, and customer counts across systems.

• Monitor pipeline health, failed jobs, delayed data, schema changes, and unexpected volume changes.

• Define response and escalation processes for data incidents and recurring quality issues.

• Work with business owners to resolve the source of data problems instead of correcting only the final report.

5. Data Governance, Security, and Documentation

• Establish practical standards for data ownership, access, classification, retention, and approved use.

• Apply role-based access controls, encryption, audit logging, and appropriate protection for personal and confidential data.

• Maintain clear data definitions, lineage, integration documentation, runbooks, and architecture diagrams.

• Partner with IT, Legal, and business leaders to support privacy, security, and compliance requirements.

• Help department leaders take ownership of the business meaning and quality of the data created within their areas.

6. Reporting, AI, and Internal Product Enablement

• Create trusted and reusable data models for reporting, dashboards, forecasting, and decision-making.

• Prepare structured and approved data for AI agents, retrieval systems, automations, and internal MicroSaaS applications.

• Prevent uncontrolled direct access to production systems by providing governed data access patterns.

• Partner with the Director of AI and internal product teams to reduce the time required to launch new data and AI use cases.

• Set standards for monitoring how AI and internal applications use company data.

7. Cross-Functional Leadership