Data Engineer

Sakata Seed America, Inc.; Woodland (CA); On-site; USD 110,000 - 155,000; Full time

Benefits offered by this job

Medical, Dental & Vision Insurance

401(k) with Company Match

Paid Vacation & Holidays

Job summary

Sakata Seed America, Inc. is seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines supporting reporting, analytics, and enterprise decision-making.

You will integrate data from multiple sources, manage lakehouse and warehouse assets, and ensure data quality and security. The role emphasizes collaboration with IT leadership, analysts, and developers to deliver reliable data solutions across the organization.

Qualifications

• Bachelor’s degree in a technical field and at least 5 years of data engineering experience.
• Experience building production-grade data pipelines and supporting critical data platforms.
• Strong SQL and data modeling skills.
• Experience with Microsoft Fabric, relational databases, and cloud data services.
• Knowledge of orchestration, scheduling, monitoring, and data quality validation.

Responsibilities

• Design, build, and maintain scalable data pipelines and data integration processes.
• Develop ETL/ELT workflows for structured, semi-structured, and file-based data.
• Implement and support Microsoft Fabric solutions, including Data Factory pipelines, Lakehouse, Warehouse, and related services.
• Design and maintain data models, schemas, and data structures for reporting and analytics.
• Collaborate with stakeholders to gather requirements and deliver high-value data solutions.
• Create and maintain technical documentation for pipelines and data definitions.
• Troubleshoot and continuously improve data platform health and reliability.

Skills

SQL

Data modeling

Python

Cloud data platforms

ETL/ELT development

Data governance

Education

Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related technical field

Tools

Microsoft Fabric

SQL Server

PostgreSQL

MySQL

Lakehouse

Data Warehouse

REST APIs

CI/CD for data engineering

Job description

Position Summary

The Data Engineer designs, builds, and maintains scalable data solutions that support operational reporting, analytics, and enterprise decision‑making. This role is responsible for developing reliable data pipelines, integrating data from internal and third‑party systems, and delivering trusted, well‑structured data for business use across the organization. The position combines hands‑on engineering, data architecture, and operational support. The Data Engineer will design ETL/ELT workflows, manage lakehouse, warehouse, and database assets, optimize performance, enforce data quality standards, and support secure, governed access to enterprise data. This role partners closely with application owners, analysts, developers, and IT leadership to translate business needs into sustainable technical solutions. A successful candidate brings strong SQL and data modeling skills, experience with cloud and hybrid data platforms, and practical knowledge of orchestration, automation, and monitoring. Experience with Microsoft Fabric, relational databases, APIs, file‑based ingestion, and modern data engineering practices is highly valued. The position requires the ability to manage multiple priorities, contribute to project delivery and operational support, document solutions clearly, and participate in maintenance or incident response activities when needed. The ideal candidate is collaborative, detail‑oriented, and committed to building resilient, efficient, and secure data solutions.

Essential Duties And Responsibilities

• Design, build, and maintain scalable data pipelines and data integration processes that move data reliably from source systems into curated analytical and operational data stores.
• Develop ETL/ELT workflows for structured, semi‑structured, and file‑based data using appropriate orchestration, transformation, and scheduling methods.
• Implement and support Microsoft Fabric solutions, including Data Factory pipelines, Lakehouse, Warehouse, Dataflow Gen2, notebooks, and related services for ingestion, transformation, and delivery of trusted data.
• Design and maintain data models, schemas, and data structures that support reporting, analytics, and downstream application needs.
• Integrate data from enterprise applications, databases, APIs, flat files, and third‑party platforms while ensuring data accuracy, completeness, and consistency.
• Optimize data queries, transformation logic, storage design, and pipeline performance to improve reliability, scalability, and cost efficiency.
• Implement monitoring, alerting, logging, and data quality checks to detect failures, anomalies, and processing issues before they affect business operations.
• Support data governance, security, and compliance requirements by applying access controls, audit practices, retention standards, and secure data handling procedures.
• Collaborate with business stakeholders, analysts, developers, and IT team members to gather requirements, define technical approaches, and deliver high‑value data solutions.
• Create and maintain technical documentation for pipelines, source mappings, transformations, data definitions, standards, and operational procedures.
• Participate in troubleshooting, root cause analysis, and continuous improvement efforts related to data platform health, performance, and service reliability.
• Contribute to standards for version control, testing, deployment, and change management for data assets and engineering workflows.
• Perform additional duties as assigned to support evolving business priorities and enterprise data initiatives.

Effectiveness and Work Management

Analytical and Problem Solving

• Demonstrate the ability to analyze data performance metrics, system behavior, and business requirements to make informed technical decisions.
• Diagnose and resolve data issues using sound logic and reasoning, including query inefficiencies, data integrity concerns, and platform‑related bottlenecks.
• Anticipate potential risks and take proactive steps to improve resilience, scalability, and operational continuity.

Initiative and Adaptability

• Take initiative when operational challenges or opportunities for improvement arise, acting decisively while protecting data availability and integrity.
• Adapt to changing business requirements, project priorities, and emerging database technologies.
• Efficiently work through formal and informal channels to remove roadblocks and support the timely delivery of database‑related solutions.

Collaboration and Communication

• Partner effectively with application owners, developers, analysts, and infrastructure teams to achieve shared business and technical goals.
• Obtain cooperation and support from internal and external stakeholders when planning, implementing, or troubleshooting database solutions.
• Maintain clear and professional communication with IT Leadership and peers to ensure alignment, transparency, and effective issue resolution.

Continuous Improvement and Professionalism

• Seek and accept feedback for personal and professional growth.
• Reflect on experiences to identify lessons learned and apply them to future situations.
• Take ownership of development and demonstrate integrity in conduct, appearance, and respectful interactions.
• Build trust and credibility through consistent performance and respectful interactions.

Execution and Accountability

• Complete data projects and assignments on time and in alignment with operational priorities and service expectations.
• Maintain accurate, up‑to‑date documentation, including data architecture, configurations, standards, backup procedures, and operational runbooks.
• Provide practical alternatives and recommendations when technical constraints arise to ensure continuity and business support.

Technical Skills

• Strong command of data querying, transformation, performance tuning, and database object development.
• Working knowledge of Python or similar scripting languages for data processing, automation, and integration tasks.
• Experience with data modeling concepts, including normalization, dimensional modeling, star schemas, and slowly changing dimensions.
• Hands‑on experience with Microsoft Fabric, relational databases, cloud data services, and modern data integration patterns.
• Understanding of orchestration, scheduling, CI/CD concepts, monitoring, and data quality validation in production environments.
• Knowledge of secure data handling, access control, governance, backup, recovery, and operational support best practices.

Communication

• Maintain clear, consistent, and professional communication with IT Leadership, team members, vendors, and business stakeholders to support department and company objectives.
• Communicate effectively across all levels of the organization, translating database concepts into practical business language when needed.
• Demonstrate a strong understanding of the Corporate vision, objectives, and core values.
• Express ideas, risks, recommendations, and technical information clearly and concisely, both verbally and in writing.
• Actively listen to stakeholders and accurately interpret operational needs, project requirements, and support issues.
• Provide customer‑focused service, ensuring a positive and professional experience from initial request through resolution.

Qualifications

Minimum Requirements and Qualifications

Education and Experience

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics, or a related technical field; equivalent practical experience may be considered.
• Minimum of 5 years of professional experience in data engineering, ETL/ELT development, data integration, database development, or a related enterprise data role, including experience building production‑grade data pipelines and supporting business‑critical data platforms.

Certifications

• Relevant Microsoft, Azure, Fabric, data, or cloud platform certifications are preferred.
• Certifications related to data engineering, security, analytics, or platform administration are a plus.

Core Competencies

• Ability to design and support scalable, maintainable data pipelines and integration workflows across multiple systems.
• Strong understanding of data quality, lineage, governance, and secure data management practices.
• Proven ability to analyze requirements, solve complex technical problems, and communicate clearly with technical and non‑technical stakeholders.
• Demonstrated ownership, attention to detail, documentation discipline, and commitment to operational reliability.

Technical Expertise

Candidates Must Demonstrate In‑depth Knowledge And Hands‑on Experience With

• Data Platforms: Microsoft Fabric, SQL Server, PostgreSQL, MySQL, cloud data services, lakehouse and warehouse platforms, or equivalent enterprise technologies.
• Data Engineering: ETL/ELT design, pipeline orchestration, ingestion frameworks, transformation logic, data quality controls, and batch or scheduled processing.
• Development: SQL, Python, stored procedures, views, functions, scripting, API integration, and automation.
• Data Modeling and Storage: Relational design, dimensional modeling, partitioning, indexing, schema design, and storage optimization.
• Operations and Reliability: Monitoring, logging, alerting, troubleshooting, backup and recovery, disaster recovery, and performance tuning.
• Security and Governance: Role‑based access, auditing, encryption awareness, retention practices, and support for compliance requirements.

Preferred Tools And Knowledge

• Experience with Microsoft Fabric components such as Data Factory pipelines, Lakehouse, Warehouse, Dataflow Gen2, notebooks, and semantic models.
• Familiarity with medallion architecture, Delta‑based storage patterns, dimensional models, and modern analytics delivery practices.
• Experience integrating data from REST APIs, flat files, ERP or line‑of‑business applications, SaaS platforms, and external data providers.
• Knowledge of source control, release management, and CI/CD practices for data engineering assets and platform changes.
• Exposure to Spark, data warehouse optimization, business intelligence platforms, and enterprise data governance tools is beneficial.

Benefits

Health & Wellness

• Medical, Dental & Vision Insurance
• Monthly Wellness Stipend
• Employee Assistance Program (EAP)
• Employee Philanthropic Giving Program
• Disability Insurance (plans vary by location)

Financial Benefits

• 401(k) Program + Company Match
• Profit Sharing Program (via 401(k))
• Holiday Bonus
• Performance Incentive Bonus Program
• Tuition Reimbursement
• 529 College‑Savings Plan
• Company‑Paid Basic Life & AD&D Insurance

Time Off & Flexibility

• Paid Vacation
• Paid Sick Leave
• 15 Paid Company Holidays
• 2 Floating Holidays
• Birthday Off