Senior Data Engineer

Hunter Agriculture•San Marcos, CA

11d•$130,000 - $145,000•Hybrid

About The Position

Hunter Industries is seeking a Senior Data Engineer who designs, develops, and maintains enterprise data systems and pipelines that enable the organization to use data as a strategic asset. This role provides technical leadership and expertise in building scalable, efficient, and governed data solutions that support analytics, reporting, and advanced use cases such as AI and machine learning. The Senior Data Engineer acts as a bridge between business needs and technology implementation, ensuring data systems are reliable, performant, and aligned with enterprise standards. This role can be performed onsite or hybrid (3 days in office) at our campus in San Marcos, CA. Candidates must be located within a commutable distance from campus.

Requirements

Bachelor’s degree in Computer Science, Data Science, Software Engineering, or related quantitative field required or a combination of education and relevant work experience.
Minimum five years’ 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 in building and optimizing scalable data pipelines and architectures on Snowflake, using ELT/ETL orchestration tools such as Azure Data Factory, and cloud object storage (e.g., OCI Object Storage, 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.
Experience planning and executing source system migrations, including source-to-target mapping, historical data conversion, validation and reconciliation, and cutover planning to minimize business disruption.
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/refresh tuning.
Strong communication and leadership skills with the ability to mentor peers and influence best practices.

Nice To Haves

Master’s degree or advanced training in data engineering, cloud platforms, or analytics preferred.
Strategic mindset with curiosity for emerging technologies, particularly AI and ML applications in data engineering, including Snowflake Cortex and Power BI Copilot.

Responsibilities

Designs, develops, and optimizes data pipelines and workflows for ingestion, transformation, and delivery of data across enterprise platforms.
Integrates structured and semi-structured data from diverse sources—Oracle Fusion (ERP/EPM), on-premises databases, and cloud applications (APIs, external systems)—into the Snowflake data platform.
Applies data cleansing, validation, enrichment, and quality controls to ensure accuracy, consistency, and trust in enterprise data.
Monitors and tunes pipelines and storage solutions for scalability, performance, and cost-effectiveness.
Implements governance, security, and compliance practices in alignment with enterprise standards.
Collaborates with architects, analysts, and data scientists to deliver solutions that support Power BI analytics, reporting, AI/ML, and self-service use cases.
Provides technical leadership by guiding best practices in pipeline design, coding standards, and system optimization.
Mentors junior engineers and contributes to knowledge sharing across the Data & Analytics team.
Evaluates and adopts emerging technologies, tools, and practices that strengthen the enterprise’s data capabilities.
Partners with business stakeholders to understand use cases, translate requirements, and deliver practical, sustainable solutions.