Data Engineer II

Servco Pacific Inc.•Honolulu, HI

2d•Hybrid

About The Position

Servco’s Data Engineers play a crucial role in ensuring the organization has the data it needs to make informed business decisions. This includes acquiring data from a variety of sources—internal systems as well as external APIs—and transforming, modeling, and curating it so it’s reliable and usable for downstream analytics and reporting. This role requires a strong understanding of DataOps practices and the ability to operate in a cloud-first, code-first, agile environment. Data Engineers in this role are expected to work with technologies such as Databricks for data warehousing, data modeling, and orchestration, as well as Cloud Functions for serverless computing and automation. Data Engineers should be skilled coders with high proficiency in Python and SQL, capable of writing clean, efficient, well-documented code and troubleshooting issues across pipelines and platforms. Data Engineers will partner closely with Analytics Engineers, who focus on downstream activities that turn prepared data into actionable insights. This role is responsible for sourcing, preparing, and serving data in a way that enables those analytics workflows to operate confidently and at scale. This position is primarily on-site in Honolulu and is not a remote role. It allows up to one day per week working from home.

Requirements

Process Mining & Stakeholder Collaboration Experience with process mining and collaborating with stakeholders to understand their needs and identify opportunities for process improvement.
Programming & Software Development Practices Experience with Python programming, including libraries such as NumPy, Pandas, and Selenium, as well as testing and debugging using tools such as PyTest. Experience with SQL programming, writing efficient queries and stored procedures. Experience with version control systems such as Git (including merging and resolving conflicts) and containers such as Docker.
Data Engineering (Integration, Modeling & Warehousing) Experience working with database integrations and designing systems that integrate data from multiple sources. Experience designing and implementing logical and physical data models and applying data warehousing best practices such as dimensional modeling, star and snowflake schemas. Experience creating and maintaining dbt documentation using Jinja templates. Experience with day-to-day database administration, including backup and recovery, performance tuning, and security management.
Cloud, Infrastructure & Architecture Experience working with cloud-based services, specifically Azure and Databricks. Experience using Infrastructure as Code (IaC) tools such as Terraform. Experience managing and maintaining analytics infrastructure, including data pipelines and storage systems, and supporting incident response procedures. Experience designing and implementing systems using a microservices architecture.
Governance & Security Experience implementing policies and procedures to ensure the availability, usability, integrity, and security of data throughout its lifecycle, including data lineage, data quality, data privacy, and compliance requirements.
Technical Documentation Experience creating and maintaining technical documentation such as project plans, requirements, design documents, and test plans.
Process Mining Ability to understand and document business processes, including identifying process inputs, outputs, and key stakeholders.
Programming (Python & SQL) Ability to work with and integrate APIs and other external systems. Ability to write efficient and optimized SQL queries and stored procedures.
Data Engineering (Orchestration, Modeling & Warehousing) Experience designing and implementing scalable, reliable, and maintainable data pipelines. Ability to identify and resolve data migration and integration issues, including data loss, corruption, and duplication. Proficient in analyzing business requirements to determine database design and implement effective data models and transformations. Ability to abstract data from its physical location and structure, allowing users to access and use the data without knowing where it resides or how it is stored. Ability to design and implement efficient, scalable database schemas. Ability to replicate data to multiple servers for disaster recovery, reporting, or data warehousing purposes.
Cloud & Software Development Practices Proficient with CI/CD pipelines and tools. Strong understanding of software design patterns and best practices for code organization, testing, and maintenance. Knowledge of serverless architecture and ability to build scalable and fault-tolerant systems using cloud-native technologies. Knowledge of cloud-native design principles, including auto-scaling, load balancing, and managed services.
Governance & Security Knowledge of data security best practices and ability to implement them in a cloud-based environment.
Infrastructure & Operations Knowledge of cloud cost optimization techniques and ability to implement them to reduce cost.
Technical Communication Strong verbal and written communication skills, and the ability to effectively communicate project status, issues, and risks to stakeholders.