Data Modeling & AI Analytics

Matrix Global•Miami, FL

10h•Remote

About The Position

We are seeking a Data Modeling & Analytics professional to join our growing Data & Analytics team. This role sits at the intersection of data engineering, analytics, and AI, transforming raw data into trusted, scalable, and well-documented data products that drive decision-making across the business. As an Analytics Engineer, you will design and maintain data models within our cloud data warehouse, develop ELT pipelines, and build data marts that support reporting, self-service analytics, data science, and emerging AI initiatives. You will partner closely with business stakeholders, analysts, and engineers to ensure our data ecosystem is accurate, accessible, and optimized for growth. This is a global role supporting data initiatives across the United States, United Kingdom, and future markets. We are building an AI-forward organization, and you will be expected to leverage AI-powered development tools to enhance productivity, improve data quality, and accelerate delivery.

Requirements

2-4 years of experience in Analytics Engineering, Data Engineering, Business Intelligence Engineering, or a similar role.
Strong experience with dbt for data modeling, transformation, testing, and documentation.
Advanced SQL skills with experience developing complex analytical datasets.
Experience with cloud data warehouses such as Amazon Redshift, Snowflake, BigQuery, or equivalent platforms.
Working knowledge of Python for data processing, automation, or analytics workflows.
Experience building and maintaining ELT/ETL pipelines using tools such as Airbyte, Fivetran, or similar platforms.
Understanding of dimensional modeling, star schemas, and analytics-focused data architecture.
Experience using Git and collaborative software development practices.
Familiarity with business intelligence and visualization tools, preferably Tableau.
Comfortable leveraging AI-powered development tools and assistants to improve productivity and workflow efficiency.
Strong communication and stakeholder management skills.
Must be based in the United States.
Bachelor's degree in Computer Science, Information Systems, Statistics, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Experience within healthcare, health technology, or other regulated industries.
Experience working with HIPAA-, GDPR-, or other compliance-regulated data environments.
Experience supporting client-facing data integrations and B2B customer onboarding.
Experience in high-growth startup or fast-paced environments.
Exposure to machine learning, predictive analytics, or AI-powered data applications.

Responsibilities

Design, develop, and maintain scalable dbt models that power reporting and analytics across the organization.
Build and manage data marts that support business intelligence, self-service analytics, predictive analytics, and AI-driven insights.
Partner with analysts and business stakeholders to translate reporting and operational requirements into reliable, reusable data models.
Develop and maintain a consistent semantic layer that enables accurate reporting and decision-making.
Build, manage, and troubleshoot ELT pipelines and integrations using Airbyte and related technologies.
Collaborate with Data Engineering teams to maintain a reliable end-to-end data platform spanning data ingestion, warehousing, transformation, and reporting.
Support onboarding of new B2B clients by developing client-specific data models, transformations, and workflows.
Monitor and optimize warehouse performance, ensuring data processing efficiency and cost effectiveness.
Implement automated testing, data validation, and quality monitoring using dbt and related tools.
Maintain data documentation, lineage, metadata, and business definitions to promote data trust and transparency.
Contribute to data governance initiatives and help establish best practices for data management across the organization.
Design data structures optimized for AI and machine learning use cases.
Leverage AI-assisted development tools such as Cursor, Claude, and similar technologies to accelerate development, testing, and documentation.
Help champion AI-enabled workflows and best practices across the Data & Analytics organization.