Senior Data Engineer

Hunter Agriculture•,

8d•$115,000 - $145,000•Remote

About The Position

Hunter Industries is seeking a Senior Data Engineer to design, develop, and maintain enterprise data systems and pipelines that enable the organization to use data as a strategic asset. The role provides technical leadership in building scalable, efficient, and governed data solutions for analytics, reporting, AI, and machine learning. It bridges business needs and technology implementation while ensuring that data systems are reliable, performant, and aligned with enterprise standards. This role is remote, and candidates must be willing to work Pacific time business hours. The position collaborates with technical teams and business stakeholders to deliver sustainable data solutions, guide engineering best practices, and strengthen enterprise data capabilities.

Requirements

Bachelor’s degree in Computer Science, Data Science, Software Engineering, or a related quantitative field, or a combination of education and relevant work experience.
At least five years of experience in data engineering, data integration, and optimization.
Proven experience developing and maintaining enterprise-scale data solutions on Snowflake, with Power BI as the primary reporting and semantic layer.
Experience integrating data from Oracle Fusion (ERP/EPM) and other on-premises and cloud applications.
Experience working with business stakeholders and senior leadership to deliver data-driven outcomes.
Demonstrated experience migrating data from legacy and enterprise source systems to modern cloud data platforms, including source-to-target mapping, data validation, and reconciliation.
Expertise building and optimizing scalable data pipelines and architectures on Snowflake, using ELT/ETL orchestration tools such as Azure Data Factory and cloud object storage such as OCI Object Storage or Azure Blob.
Proficiency in SQL and Python, with strong experience in Snowflake SQL, dbt for transformation, and pipeline orchestration tools such as Azure Data Factory or Airflow.
Strong knowledge of relational databases and enterprise source systems, including Oracle Fusion (ERP/EPM) and SQL Server.
Familiarity with data governance and security frameworks, including metadata, lineage, role-based access controls, and privacy-by-design, applied within Snowflake and the Power BI semantic model.
Ability to collaborate across technical and business teams and translate requirements into sustainable solutions.
Skill in diagnosing and resolving performance issues in cloud-based data environments, including Snowflake query and warehouse optimization and Power BI semantic model and refresh tuning.
Strong communication and leadership skills, including the ability to mentor peers and influence best practices.
Willingness to work Pacific time business hours.

Nice To Haves

Master’s degree or advanced training in data engineering, cloud platforms, or analytics.
Curiosity about emerging technologies, particularly AI and ML applications in data engineering, including Snowflake Cortex and Power BI Copilot.

Responsibilities

Design, develop, and optimize data pipelines and workflows for ingesting, transforming, and delivering data across enterprise platforms.
Integrate structured and semi-structured data from Oracle Fusion (ERP/EPM), on-premises databases, cloud applications, APIs, and external systems into Snowflake.
Apply data cleansing, validation, enrichment, and quality controls to ensure enterprise data accuracy, consistency, and trustworthiness.
Monitor and tune pipelines and storage solutions for scalability, performance, and cost-effectiveness.
Implement governance, security, and compliance practices aligned with enterprise standards.
Collaborate with architects, analysts, and data scientists to deliver solutions for Power BI analytics, reporting, AI/ML, and self-service use cases.
Guide best practices in pipeline design, coding standards, and system optimization.
Mentor junior engineers and contribute to knowledge sharing across the Data & Analytics team.
Evaluate and adopt emerging technologies, tools, and practices that strengthen enterprise data capabilities.
Partner with business stakeholders to understand use cases, translate requirements, and deliver practical, sustainable solutions.
Plan and execute source system migrations, including source-to-target mapping, historical data conversion, validation, reconciliation, and cutover planning to minimize business disruption.