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.
This position is primarily on-site and is not a fully remote role. Team members may work from home up to one day per week.
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
Visit www.servco.com/careers to apply online.
Pay Range: $72,000 - $96,000
