Data Engineer

Data Engineer

Compensation starting at $83,000 annually

Sponsorship: This position is not eligible for immigration sponsorship.

We're seeking a Data Engineer to design, develop, and optimize enterprise data pipelines, system integrations, APIs, and data warehousing solutions. In this role, you'll improve how data moves across platforms, enabling reliable reporting, real-time analytics, automation, and informed business decisions.

What You'll Do

Develop and maintain scalable ETL/ELT pipelines and system integrations.

Design secure API-driven integrations with internal systems and external partners.

Support data warehousing, analytics, reporting, and business intelligence needs.

Implement real-time and batch data integrations.

Monitor and resolve data quality, reliability, and performance issues.

Establish data and API governance standards, including security, access control, versioning, and documentation.

Identify opportunities to automate workflows and improve operational efficiency.

Collaborate with IT, BI, and business teams to deliver effective data solutions.

Document technical designs, integration patterns, and operating procedures.

What You'll Bring

Three to five years of experience in data engineering, system integration, or automation.

Strong SQL, data modeling, and query optimization skills.

Hands-on experience developing ETL/ELT solutions using Azure Data Factory.

Experience with Snowflake, Azure SQL, SQL Server, or comparable cloud and on-premises data platforms.

Experience integrating REST APIs using authentication methods such as OAuth, API keys, and JWT.

Python experience related to API connectivity, automation, file processing, or data transformation.

Familiarity with Power BI, Power Automate, Azure Logic Apps, Git, Azure DevOps, and CI/CD practices.

Understanding of data security, role-based access controls, and regulatory compliance.

Ability to manage multiple initiatives and collaborate effectively in an Agile environment.

Experience with AI-enabled data solutions and preparing data for machine-learning workloads is a plus.