Data Engineer I

Must reside on Oahu – Hybrid role/Work from home up to one day per week

Servco’s Data Engineers help ensure the organization has trusted, reliable data to support business decisions. This includes acquiring data from internal systems and external APIs, then transforming, modeling, and curating it for analytics, reporting, AI, and other data-driven solutions.

This role requires foundational DataOps knowledge and the ability to contribute in a cloud-first, code-first, agile environment. The Data Engineer I works with tools such as Databricks, Python, SQL, data orchestration, warehousing, and cloud-native technologies, while continuing to develop clean, efficient, well-documented coding practices.

The ideal candidate brings curiosity, creative and critical thinking, and a strong interest in building quality data solutions. They are motivated to learn how data moves across the organization and to contribute to the reliability of Servco’s core data infrastructure.

As a Level I Data Engineer, this individual develops a working understanding of Servco’s technical systems, business operations, and related dependencies. They perform core responsibilities with increasing independence while continuing to receive guidance, mentorship, and review.

This is a junior-level role for someone who may not yet have every skill or full proficiency with every technology used by the team. The successful candidate is eager to learn, seek feedback, and grow into a long-term data engineering career aligned with Servco’s AI-first future.

This role supports data quality, availability, and trust across the organization, enabling better decisions and supporting strategic, data-driven and AI-enabled outcomes.

KEY OUTCOMES :

Contribute to the design, development, and maintenance of data pipelines that move data from source systems to storage and processing environments. Assist with logical and physical data structures that support organizational reporting, analytics, warehousing, and cloud storage needs. Support reliable data integration across systems, applications, and departments. Help ensure data is accurate, complete, secure, and usable by the organization. Monitor and improve the performance of data pipelines and storage systems with guidance. Assist with deployment, maintenance, and documentation of data infrastructure. Participate in planned maintenance and provide occasional after-hours support for business-critical data operations, production incidents, and monitoring alerts. Support expectations, escalation procedures, and any on-call rotation will be communicated in advance whenever practicable. Partner with Analytics Engineers to support downstream analytics, reporting, and data quality needs. Monitor the health and performance of assigned infrastructure components, including cloud services, data pipelines, and related applications. Explore, test, and apply new tools or methods that may improve analytics and data processing capabilities. Contribute to data governance and stewardship by supporting data quality, completeness, security, and compliance standards.

QUALIFICATIONS :

• •Bachelors in Computer Science, Information, Data Science, Business Analytics or Information Management preferred; equivalent education and experience may be considered.
• •1–2 years of experience in an individual contributor capacity, with exposure to the following areas:

Process mining

Collaborating with stakeholders to understand needs and identify process improvement opportunities

Gathering and clarifying basic business requirements and translating them into data pipeline, data model, or reporting support needs

SQL programming

Experience working with database integrations and the ability to import and export data from various sources

Python programming

Experience with Python libraries and frameworks for data manipulation, analysis, visualization, and automation, such as NumPy, Pandas, and Selenium

Experience with testing and debugging Python code, including the use of tools such as PyTest and debugging libraries

Industry Standard Software Tooling and Development Practices

Familiarity with Git-based development workflows, including branches, pull requests, peer review, and resolving basic merge conflicts

Data orchestration and integration

Foundational knowledge of data integration patterns and ability to contribute to data integrations from multiple sources

Data transformation and modeling

Exposure to data modeling concepts and ability to assist with logical and physical data models

Data warehouse management

Experience with data warehousing concepts and best practices, such as data normalization, dimensional modeling, and star and snowflake schemas

Experience with creating and maintaining dbt documentation, which includes the use of Jinja templates for creating tables, columns, and relationship documentation

Database management

Foundational understanding of database administration concepts, including backup and recovery, performance, and security considerations

Industry standard cloud tooling and development practices

Exposure to cloud-based services, preferably Azure and Databricks

Exposure to Infrastructure as Code tools, such as Terraform, preferred

Governance and security

Foundational knowledge of data governance, security, lineage, quality, privacy, and compliance practices

Infrastructure management and incident response

Ability to support the infrastructure used for analytics systems, including cloud services, data pipelines, and storage systems

Ability to follow incident response procedures, including identification, classification, escalation, and recovery support

System design and architecture

Foundational understanding of system design concepts, including microservices and loosely coupled architecture

Skills:

Process mining and requirements gathering

Ability to document business processes, inputs, outputs, and key stakeholders

Python programming

Ability to work with APIs and external systems, including authentication, pagination, retries, error handling, and rate-limit considerations

Cloud tooling and development practices

Foundational knowledge of serverless and cloud-native architecture, with ability to contribute to scalable, reliable solutions

Software tooling and development practices

Familiarity with CI/CD concepts, peer review, code organization, testing, and maintainable development practices

SQL programming

Ability to write, review, and troubleshoot SQL queries with guidance

Data orchestration and integration

Ability to contribute to reliable, maintainable data pipelines and troubleshoot common migration or integration issues

Data transformation and modeling

Ability to support data model and transformation work based on business requirements

Data warehouse and database concepts

Foundational knowledge of data warehousing, schemas, data abstraction, replication, and recoverability concepts

Governance, security, and incident support

Ability to follow data security practices, perform first-level troubleshooting, and escalate issues appropriately

Technical communication

Clear verbal and written communication skills, including the ability to share project status, issues, and risks with stakeholders

Licenses and Certifications:

• •Databricks Data Engineer Associate Certification preferred, but not required
• •Databricks Data Engineer Professional Certification preferred, but not required

At Servco, we’re committed to providing valuable mobility solutions to empower people through the freedom of movement and opportunity. From Australia to California, and of course, Hawaii, Team Servco is a collective of over 2,000 like-minded individuals guided by our four Core Values of Respect, Service, Teamwork, and Innovation. For over 100 years, we have been dedicated to superior service, to both our customers and team members. We look fo