Data Engineer I

Apple Federal Credit Union•Fairfax, VA

23h

About The Position

Under the general supervision of the Manager, AI & Data Engineering and in alignment with Apple FCU’s mission and values, the Data Engineer I will support the design, development, and maintenance of Apple FCU’s enterprise data warehouse/lakehouse solutions. This role contributes to initiatives that integrate data from multiple sources into a trusted enterprise “source of truth,” primarily by assisting with repeatable ingestion and ELT/ETL processes, foundational data modeling, and the development and documentation of SQL-based transformations and datasets. The Data Engineer I will help ensure reliability and trust in the data platform by implementing and following data quality checks and monitoring routines, triaging pipeline issues, and escalating complex problems appropriately. The role will collaborate with senior engineers, data analysts, and stakeholders to clarify requirements and ensure delivered datasets and pipelines align with business intent, established standards, and maintainability expectations. The Data Engineer I will also contribute to the team’s adoption of engineering best practices (documentation standards, repeatable deployments, and basic CI/CD hygiene as assigned) and will help maintain well-governed datasets that enable analytics today and provide foundational readiness for future AI-enabled consumption patterns. The candidate will be expected to perform their duties with a mindset that reflects The Apple Way principles: Team Up, Serve with Purpose, Challenge Yourself, and Own It. A keen awareness of and compliance with credit union policies and procedures, as well as regulations pertaining to the Bank Secrecy Act, is imperative. Additionally, Data Engineer I will undertake other responsibilities as delegated by the Manager, AI & Data Engineering.

Requirements

1–3 years of relevant experience (internships/co-ops count) in one or more of the following: software development, data engineering, data warehousing, analytics engineering, or data integration.
Foundational knowledge of SQL and relational database concepts; ability to write and troubleshoot basic queries in support of data ingestion and transformation efforts (with coaching as needed).
Familiarity or exposure to data pipeline/ELT/ETL concepts (e.g., ingestion, transformations, scheduling, orchestration, logging).
Exposure to cloud data platforms and services (preferably Microsoft Azure and/or Microsoft Fabric) and willingness to learn Apple FCU’s modern data stack.
Working knowledge of software engineering fundamentals (source control such as Git, basic branching, code review concepts).

Responsibilities

Support the design, development, and maintenance of Apple FCU data warehouse / lakehouse solutions under the guidance of senior engineers, contributing to initiatives that integrate data from multiple sources into an enterprise “source of truth.”
Assist in building and maintaining data ingestion and ELT/ETL pipelines, including the implementation of repeatable processes to collect data from internal and external sources.
Contributes to efforts that transform the data warehouse into a central, certified repository, supporting reporting, analytics, and self-service consumption patterns.
Implement and follow data quality checks and monitoring routines to help ensure data consistency and reliability across systems; triage pipeline failures and escalate complex issues appropriately.
Support the team’s adoption of engineering best practices (documentation standards, repeatable deployments, and basic CI/CD hygiene as assigned).
Develop, test, and document SQL queries, transformations, and data models to support downstream reporting and analytics needs.
Partner with data analysts and other stakeholders to clarify requirements and ensure delivered datasets and pipelines meet business intent and quality standards.
Participate in the creation and upkeep of data standards, patterns, and operational runbooks, contributing to organizational continuity and maintainability.
Contribute to foundational enablement of AI/ML initiatives by helping maintain reliable, well-governed datasets and pipelines that can support analytics and future AI tooling.