POSITION SUMMARY 

The Data Engineer plays a critical role in designing, building, and maintaining reliable, scalable, and high-quality data pipelines that support PTR’s enterprise-wide analytics platform. Working closely with the Lead Data Architect and the Business Intelligence team, this role implements modern data integration and transformation workflows using PTR’s data stack, including Fivetran, Snowflake, dbt, Metaplane, Astronomer, and Sigma. 

The position also supports and maintains existing data processes within the legacy environment to ensure business continuity during the transition to a modern data warehouse. This role is well-suited for an engineer who enjoys building, problem-solving, and optimizing data systems in a collaborative, fast-paced, and evolving environment.   

LOCATION 

• Remote (US-based, Central or Eastern Time Zone preference)
• 5% travel to company sites may be required   

COMPENSATION

This position offers a competitive compensation package, benchmarked to regional market standards. It consists of a base salary plus the opportunity for quarterly profit sharing after one year of employment.

RESPONSIBILITIES: 

• Pipeline Development: Design, build, and maintain robust ELT pipelines using FiveTran, dbt, and Astronomer (Airflow) to ensure accurate and timely data delivery to the Snowflake data warehouse. 
• Data Operations: Support existing data processes in legacy systems, ensuring data reliability and service continuity during transition and decommissioning.
• Data Integration: Develop automated workflows to integrate data from multiple sources into Snowflake, working with external APIs, databases, and enterprise systems.
• Transformation & Modeling: Implement and maintain dbt models aligned with dimensional design standards and business requirements defined by the Lead Data Architect and BI analysts.
• Quality & Observability: Partner with the architect and BI teams to establish robust data quality and observability practices using Metaplane, ensuring trust in all published data assets.
• Monitoring & Maintenance: Manage data pipeline runs, monitor system health, and troubleshoot issues related to performance, scheduling, and data freshness.
• Collaboration: Partner cross-functionally with BI analysts and data stakeholders to understand business needs and ensure that engineering work supports business priorities.
• Documentation & Versioning: Maintain clear documentation of data pipelines, data mappings, and transformation logic, leveraging Git-based version control and CI/CD practices.
• Continuous Improvement: Identify opportunities to automate manual processes, optimize data performance, and enhance system scalability.
• Legacy Transition Support: Assist in incremental migration of data and processes from on-premises or legacy systems to the modern cloud data platform.   

REQUIREMENTS 

MUST HAVE 

• Bachelor's degree in computer science, Information Systems, or a related field (or equivalent work experience).
• 5+ years of experience in data engineering, ETL/ELT development, or backend data platform operations.
• Practical experience with Snowflake, Fivetran, and dbt in building and deploying production data pipelines.
• Familiarity with Astronomer (Apache Airflow) or similar workflow orchestration tools for data pipeline scheduling and monitoring.
• Strong SQL proficiency for data transformation, modeling, and optimization.
• Experience maintaining and enhancing data pipelines in hybrid or legacy environments.
• Hands-on experience optimizing query performance and managing large datasets.
• Understanding modern data modeling techniques (Star, Snowflake schemas).
• Experience with version control (Git) and CI/CD practices for data workflows.
• Strong problem-solving skills, detail orientation, and an ownership mindset in production data operations.
• Effective communication and collaboration skills with technical and non-technical stakeholders.   

NICE TO HAVE 

• Experience with Metaplane or other data observability tools.
• Familiarity with BI tools such as Sigma, Power BI, or Tableau.
• Exposure to Python scripting for automation or light transformation logic.
• Experience supporting legacy data warehouses (e.g., SQL Server, PostgreSQL) during modernization efforts.
• Knowledge of cloud environments (AWS, Azure, or GCP) and associated data services.
• Understanding of data governance, metadata management, and data privacy compliance standards.