Data Engineer

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Full Time Professional Hollywood, FL, US

Salary: $ Annually

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 .

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 .

Critical business data currently lives across Microsoft Dynamics 365 Business Central, Hub Spot, Optimizely, Big Commerce, 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 Micro

SaaS applications that depend on trusted data .

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 .

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 .

Build and maintain reliable data pipelines connecting Business Central, Hub Spot, 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 .

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 .

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 .

Create trusted and reusable data models for reporting,…