Junior Data Analytics Engineer

Junior Data Analytics Engineer

Analytics Engineering & Data Quality

Oahu, Hawaiʻi | Full-Time | Hybrid
$70,000–$90,000 annually

The Opportunity

At Hawaii Foodservice Alliance (HFA), the work we do behind the scenes helps keep Hawaiʻi’s food supply chain moving every day. Data plays an important role in helping our teams understand the business, solve problems, and make informed decisions.

We’re looking for a Junior Data Analytics Engineer to build and maintain the trusted analytical datasets that support reporting, internal applications, data science, and AI-enabled systems.

You will work within an established cloud data environment to transform operational data into clean, documented, and reusable analytical products.

This is a hands-on build-and-own role. You’ll work with real operational data within an established cloud data environment and collaborate with technical and business teams across HFA.

What You’ll Do

Analytical Data Modeling

• Build and maintain staging, intermediate, dimensional, fact, and analytics-layer data models.
• Transform data from operational systems into trusted, business-ready analytical datasets.
• Develop reusable data marts and data products for reporting, internal applications, operational analysis, data science, and AI agents.
• Follow established standards for model structure, naming, keys, data grain, lineage, and materialization.
• Learn and apply dimensional-modeling concepts such as star schemas, facts, dimensions, natural keys, surrogate keys, and slowly changing dimensions.
• Turn recurring analytical questions into governed, reusable data models instead of repeatedly creating one-time queries.

Data Quality and Validation

• Reconcile analytical datasets against source systems, existing reports, and known operational outcomes.
• Investigate unexpected totals, missing records, duplicate data, changing trends, and other anomalies.
• Write and maintain automated tests for data freshness, completeness, uniqueness, relationships, accepted values, and required fields.
• Build recurring validations and monitoring that identify data-quality problems before downstream users encounter them.
• Document the cause, impact, and resolution of data issues.
• Communicate clearly when a result cannot yet be trusted and explain what is needed to validate it.
• Help distinguish among source-data issues, integration failures, modeling errors, business-process changes, and legitimate changes in business activity.

Analytics and Business Support

• Write SQL to answer business, operational, and troubleshooting questions.
• Work with business teams to understand how activities, transactions, customers, products, locations, and other operational concepts are represented in the data.
• Translate loosely defined business questions into specific, measurable, and queryable requirements.
• Use established business definitions and raise questions when definitions are missing, inconsistent, or ambiguous.
• Explain what a dataset includes, what it excludes, how it should be used, and where its limitations are.
• Support reporting and analytical products by providing well-shaped, clearly defined, and thoroughly tested datasets.
• Help end users understand and appropriately use analytical information.

Automated Analytical Systems

• Build scheduled datasets, recurring analyses, business-rule checks, exception monitoring, and automated analytical workflows.
• Develop analytical products that can be consumed by people, dashboards, internal applications, predictive models, and AI agents.
• Help create structured semantic context that allows analytical tools and AI systems to interpret business data reliably.
• Convert successful exploratory analysis into maintainable and repeatable production processes.
• Assist with the development of alerts and monitoring when important data, metrics, or operational patterns move unexpectedly.

Support for Data Science and Agentic Systems

• Prepare clean historical datasets for forecasting, machine learning, optimization, and other applied data-science initiatives.
• Help define and validate model features, training datasets, evaluation datasets, and production inputs.
• Assist with validating model outputs against source data and actual business outcomes.
• Create reusable analytical datasets that support predictive and prescriptive decision systems.
• Support the testing of AI agents that use structured enterprise data.
• Help develop expected results, evaluation cases, regression tests, and validation datasets for agentic systems.
• Work with senior technical specialists to ensure AI agents use governed definitions and trusted data sources.

Engineering Practices

• Use Git, branches, pull requests, peer review, and controlled deployment processes.
• Write readable, maintainable, and well-organized SQL and Python.
• Maintain technical documentation, model descriptions, lineage, and business definitions as part of the development process.
• Participate constructively in code reviews and incorporate feedback into your work.
• Use AI-assisted coding tools thoughtfully to accelerate development, testing, troubleshooting, and documentation.
• Review, understand, test, and validate AI-generated work rather than accepting it without verification.
• Follow established data-security, access-control, privacy, and change-management practices.
• Work within the conventions of an existing production environment while helping improve those conventions over time.

Cross-Team Collaboration

• Support the senior data-platform specialist with data validation, database analysis, documentation, security reviews, and agent testing.
• Partner with the Data Engineer on ingestion issues, pipeline dependencies, source-system changes, and data availability.
• Partner with the Senior Applied Data Scientist on model inputs, historical datasets, evaluation data, and analytical outputs.
• Work with software, reporting, and business teams to ensure analytical products meet real operational needs.
• Escalate issues with a clear explanation of the symptoms, likely cause, affected users, business impact, and work already completed.

What We’re Looking For

• Zero to three years of professional experience, internship experience, academic work, bootcamp training, or equivalent self-directed project experience.
• Education or demonstrated experience in data analytics, analytics engineering, computer science, statistics, information systems, mathematics, engineering, business analytics, or a related discipline.
• Working knowledge of SQL, including:
• Joins
• Aggregations
• Grouping
• Subqueries
• Common table expressions
• Conditional logic
• Basic window functions
• Comfort using Python for scripting or data analysis, or clear evidence of the ability and interest to learn it quickly.
• Strong analytical habits and attention to detail.
• A willingness to check your work, question unexpected results, and investigate discrepancies.
• Ability to break a larger problem into specific, testable steps.
• Ability to take a general business question, ask useful clarifying questions, and translate it into a concrete analytical task.
• Interest in learning how business activity is represented across operational and analytical systems.
• Willingness to learn an existing production codebase and work within established conventions.
• Clear written and verbal communication.
• Ability to describe what changed, why it changed, how it was tested, and who may be affected.
• A learner’s mindset, including a willingness to ask questions early, receive constructive feedback, and follow through on that feedback.
• Interest in using AI-assisted development tools as an accelerator while maintaining independent understanding and verification.
• Ability to work independently on a well-defined task while collaborating closely with technical and business teams.

Preferred Experience

We do not expect an entry-level candidate to have experience with every item below. Exposure through coursework, internships, personal projects, or professional work is valuable.

• dbt or a comparable data-transformation framework
• Dimensional modeling and star-schema concepts
• Cloud data warehouses such as Snowflake, BigQuery, Redshift, Databricks, or similar platforms
• Git, branching, pull requests, and code review
• Python data tools such as pandas, Polars, or Jupyter
• Data visualization and business-intelligence platforms
• Workflow orchestration and scheduled data pipelines
• Data-ingestion, replication, or integration tools
• Automated data testing and observability
• SQL Server or another relational database platform
• APIs, structured files, or system-to-system data exchanges
• ERP, transportation, warehouse, inventory, merchandising, sales, or other operational data
• Statistics, forecasting, optimization, or machine-learning coursework
• Semantic models, metric definitions, metadata, or data catalogs
• LLM applications, AI agents, retrieval systems, or AI-assisted software development
• Retail, grocery, wholesale distribution, logistics, or another operationally complex industry

Why HFA?

You will work with real operational data in an established production environment. The datasets you build will support people making decisions about customers, products, inventory, purchasing, sales, distribution, and company performance.

It’s an opportunity to continue building your technical skills while working alongside technical and business teams and seeing how the data connects to HFA’s day-to-day operations across Hawaiʻi.

HFA is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age citizenship, marital status, disability, gender identity or Veteran status.