Data Engineer

Tergar International•., MN

1d•$50,000 - $56,000•Remote

About The Position

We are looking for a skilled and motivated Data Engineer to join our dynamic data team. The ideal candidate will have experience in designing, building, and maintaining scalable data pipelines and infrastructure, ensuring high data quality, and working collaboratively with various teams to support business intelligence and analytics initiatives.

Requirements

Linux (Bash)
3+ years of experience in data engineering, ETL/ELT development, analytics engineering, or a related technical role.
Strong proficiency in Python, PySpark, and JavaScript.
Advanced SQL skills and hands-on experience with relational and analytical databases such as PostgreSQL, MySQL, or Amazon Redshift.
Practical experience working with AWS cloud services, including AWS Lambda, Glue Jobs, and Workflows.
Experience using version control systems such as Git and working with CI/CD pipelines for automated testing and deployment.
Solid understanding of data warehousing, data modeling, database design, and performance optimization best practices.
Familiarity with data governance, security, privacy, and compliance standards.
Strong problem-solving and analytical skills, with excellent attention to detail.
Clear communication skills and the ability to collaborate effectively with technical and non-technical team members.

Nice To Haves

Experience working with CRM platforms, especially HubSpot, including familiarity with the HubSpot API.
Knowledge of Power BI
Experience working with Docker
Experience implementing and managing tracking and analytics tools such as Google Tag Manager, PostHog, or similar services.
Proficiency in Terraform-based infrastructure deployment and automation.

Responsibilities

Design, build, and maintain reliable and scalable ETL (extract, transform, load) pipelines to collect, process, and store large volumes of structured and unstructured data from multiple sources.
Develop, optimize, and maintain the data warehouse architecture, ensuring data integrity and availability.
Collaborate with data scientists, analysts, and business stakeholders to understand data needs and deliver tailored data solutions.
Ensure high-quality data by implementing robust data validation, testing, and monitoring processes.
Implement and manage cloud-based data platforms and databases for large-scale data storage and processing.
Develop and maintain data models and database schemas to support analytics, reporting, and machine learning use cases.
Optimize and monitor performance of data pipelines and queries, ensuring timely delivery of data.
Implement and enforce data governance, security, and privacy practices in compliance with industry standards and regulations (e.g., GDPR, CCPA).
Automate data collection, transformation, and delivery processes, enhancing efficiency and minimizing manual intervention.
Document data workflows, architectures, and processes for cross-functional teams and stakeholders.