Data Engineer

Job Summary

We are seeking a Data Engineer who thrives on building systems and solving complex data challenges. This is a development-first role focused on writing production-ready code, designing reliable pipelines, and transforming raw data into business-ready formats. The ideal candidate is self-directed, embraces AI-assisted development tools, and takes ownership of projects end-to-end while collaborating effectively when needed.

Roles and Responsibilities

• Write clean, production-ready Python and TypeScript/Node.js code

• Build and maintain ETL/ELT pipelines

• Develop integrations between Salesforce, SQL Server, and internal systems

• Implement automated alerting and monitoring

• Transform raw data into usable business formats

• Build validation and quality checks into pipelines

• Troubleshoot and resolve data issues with permanent fixes

• Document work for maintainability

• Use AI coding tools to accelerate development and improve code quality

• Automate repetitive tasks to focus on problem-solving

• Stay current with emerging tools and techniques

• Lead projects from requirements through deployment

• Make and communicate technical decisions clearly

• Partner with analysts, engineers, and stakeholders to meet business needs

• Proactively suggest improvements and better approaches

Education, Skills and Experience

• 3 years of professional experience in data engineering or related development

• Expert-level Python proficiency

• Strong TypeScript/Node.js skills

• SQL Server expertise including queries, performance tuning, and stored procedures

• Salesforce data experience

• Deep understanding of ETL patterns and frameworks

• Production mindset with focus on error handling, logging, and monitoring

• Ability to work independently with minimal direction

• Technical environment includes Python (pandas, SQLAlchemy), TypeScript/Node.js, SQL Server (T-SQL, SSIS), Salesforce APIs, Git version control, CI/CD pipelines, and AI coding assistants

• Preferred experience with Snowflake or other cloud data warehouses, AWS data services, orchestration tools such as Apache Airflow, exposure to data science concepts, and integration platforms such as Mulesoft or Biztalk