Data Engineer

World Emblem Internatio•Hollywood, FL

20d•$0 - $190,000•Onsite

About The Position

World Emblem International is seeking a Senior Data Engineer / Data Architect to be the hands-on technical owner of the company's enterprise data foundation. This role involves assessing the current data environment, defining the future architecture, and building the necessary pipelines, models, controls, and data services to ensure data accuracy, security, and usability. The position requires not only architectural design but also the practical implementation of core data pipelines, models, tests, and services. The need for this role arises from critical business data residing across multiple disparate systems, leading to potential issues like duplicate records, conflicting definitions, incomplete reporting, security risks, and technical debt, which become more significant as the company advances its AI and internal software initiatives.

Requirements

Seven or more years of experience in data engineering, including experience designing and owning production data platforms.
Strong hands-on skills in SQL, Python, Apache Spark, Apache Airflow.
Experience building and supporting ETL or ELT pipelines, APIs, webhooks, and scheduled or event-driven integrations.
Strong understanding of data modeling for operational systems, analytics, and shared enterprise data.
Experience with cloud data platforms, data warehouses, lakehouses, or similar modern data environments.
Experience implementing data quality tests, reconciliation, monitoring, alerting, and incident response.
Working knowledge of security, access control, encryption, privacy, retention, and audit requirements.
Experience with Git, code review, automated testing, and deployment pipelines.
Ability to explain complex technical issues clearly to executives, business leaders, and nontechnical teams.
Proven ability to work independently, set priorities, document decisions, and deliver working systems.
Bachelor's degree in computer science, information systems, engineering, or a related field, or equivalent practical experience.

Nice To Haves

Experience integrating Microsoft Dynamics 365 Business Central or another enterprise ERP platform.
Experience with HubSpot, ecommerce platforms, digital marketing platforms, or manufacturing data.
Experience with Microsoft Fabric, Azure Data Factory, Azure Synapse, Databricks, Snowflake, BigQuery, Redshift, or a comparable platform.
Experience with transformation and orchestration tools such as dbt, Airflow, Dagster, or similar technologies.
Experience building Power BI semantic models or another governed reporting layer.
Experience preparing enterprise data for AI, retrieval-augmented generation, vector search, agents, or machine learning applications.
Experience with master data management, metadata catalogs, data lineage, data contracts, and schema governance.

Responsibilities

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, 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.
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, 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.
Work closely with the CTO, Infrastructure, Applications, Finance, Operations, Ecommerce, Marketing, Sales, and other business teams.
Translate technical data issues into clear business risks, options, costs, and recommended actions.
Review vendor-built integrations and hold partners accountable for documentation, security, maintainability, and delivery quality.
Create standards and training that improve how teams collect, define, share, and use data.