Manager, AI & Data Engineering

Apple Federal Credit Union•George Mason, VA

About The Position

At Apple Federal Credit Union, we’re more than a financial institution; we’re a community-focused organization powered by passionate people. With 24 branches across Northern Virginia and a proud legacy of service, we’re committed to improving the lives of our members and the communities we serve. We believe our employees are our greatest asset. That’s why we foster a supportive workplace culture that values inclusiveness, innovation and growth. Whether you’re just starting out or advancing your career, you’ll find opportunities for professional development, mentorship and meaningful impact. Under the general supervision of the Director, Data Analytics & Enterprise Architecture, and in adherence to established policies and procedures, the Manager, AI & Data Engineering will lead Apple FCU’s data engineering capabilities and drive the adoption of AI engineering practices that transform how staff consume enterprise data. This role is accountable for building and operating a high-performing engineering team that delivers reliable, governed, and scalable data products and enables AI-powered, natural-language access to trusted insights. This position owns the engineering operating model and delivery execution, including work intake, prioritization, capacity planning, delivery predictability, and operational maturity. The Manager establishes and enforces engineering standards (documentation, testing discipline, repeatable deployments, runbooks, and data quality validation) and coordinates delivery across technical teams and stakeholders to ensure work is scoped, sequenced, and aligned to business outcomes. A core responsibility is treating AI as an engineered capability, not an experiment, by setting pragmatic tooling and platform direction, establishing “golden path” delivery patterns, and ensuring production-grade supportability (architecture, Azure/Fabric alignment, compute/runtime considerations, observability, and cost-awareness). The role partners with leadership to balance innovation with governance, security, and reliability. 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, the Manager, AI & Data Engineering will undertake other information technology responsibilities as delegated by the Director, Data Analytics & Enterprise Architecture.

Requirements

7–10 years of demonstrated, relevant experience in data engineering, data platform engineering, and/or software engineering, including enterprise data ingestion, transformation, and operational support.
2+ years of people-lead / management experience (or equivalent demonstrated team leadership), including coaching, feedback, hiring, and performance management in a technical team environment.
Strong working knowledge of SQL and relational data concepts, with demonstrated experience driving data quality, reliability, monitoring, and operational maturity (health checks, incident patterns, root-cause remediation, prevention-oriented improvements).
Experience building and operating data solutions on modern cloud data platforms (preferably Microsoft Azure and/or Microsoft Fabric), including orchestration patterns, performance considerations, and production support expectations.
Demonstrated ability to manage team workflow and capacity (intake, prioritization, sprint/iteration planning, WIP management, dependency management, delivery predictability, and stakeholder communication).
Demonstrated ability to lead the adoption of AI engineering practices as a delivery capability, including pragmatic tool/platform decision-making, “golden path” patterns, and production-grade supportability (architecture, integrations, compute/runtime considerations, observability, and cost awareness).
Working knowledge of responsible AI expectations and ability to ensure AI-enabled solutions align with organizational policy, governance, security, and privacy requirements.
Strong cross-functional collaboration skills; demonstrated ability to translate business needs into engineered outcomes, align stakeholders, communicate risks/tradeoffs, and drive execution across dependencies.
BA/BS degree with an emphasis in IT/IS, Computer Science or equivalent combination of experience and relevant certifications

Nice To Haves

Experience working within a financial institution or similarly regulated environment is preferred, including awareness of operational discipline and policy-driven controls.

Responsibilities

Lead, coach, and develop the Data Engineering function, fostering a collaborative, high-performing engineering culture aligned to The Apple Way (Team Up, Serve with Purpose, Challenge Yourself, Own It).
Recruit, interview, and onboard engineering talent as needed; maintain balanced capability across ingestion, transformation, platform operations, and AI-enabled engineering practices.
Establish clear expectations for engineering craftsmanship and accountability (quality, documentation, testing discipline, code review norms, operational readiness).
Provide routine performance feedback and formal reviews; create development plans and growth pathways for team members and ensure continuity of knowledge across the function.
Own and operate the team’s work intake, prioritization, and capacity planning processes (backlog health, sprint/iteration planning, WIP management, dependency visibility, and delivery predictability).
Coordinate delivery across Data Engineering, Data Analytics, and business stakeholders to ensure work is appropriately scoped, sequenced, and aligned to business outcomes.
Provide routine reporting and communication of delivery status, risks, tradeoffs, and outcomes; ensure stakeholders have consistent visibility into priorities and timelines.
Identify and remove blockers (technical, process, resourcing, cross-team dependencies) and drive continuous improvement to increase throughput and reduce cycle time without sacrificing quality.
Maintain a “production-minded” operating rhythm: clear definition of done, handoff readiness, and supportability expectations for delivered work.
Oversee data platform reliability and operational maturity: monitoring patterns, incident triage practices, root-cause remediation, and prevention-oriented improvements to reduce recurring issues.
Drive data quality consistency and trust across systems by establishing validation patterns, health checks, and governance-friendly workflows that increase confidence in certified datasets.
Ensure operational procedures and runbooks exist and are followed for critical pipelines, data products, and platform services; improve support readiness over time.
Guide the team in implementing repeatable data engineering patterns (ingestion, orchestration, transformations, and publish/consume standards) that improve maintainability and reduce technical debt.
Partner with stakeholders as needed to resolve escalations and ensure timely recovery from failures, with clear communication and documented outcomes.
Lead the evolution of AI engineering practices and patterns that transform how staff consume enterprise data (e.g., natural-language interaction and AI-assisted access to governed insights), treating AI as a production capability rather than an experiment.
Establish pragmatic “golden path” standards for AI solution delivery: tool selection principles, integration patterns, secure data access patterns, evaluation/testing expectations, and operational support requirements.
Ensure AI-enabled solutions are delivered with production-grade discipline (architecture, Azure/Fabric alignment, compute/runtime considerations, observability/telemetry, and cost-awareness).
Drive responsible and secure adoption of AI-assisted development practices within the engineering function (automation, documentation acceleration, guardrailed experimentation) to improve productivity while maintaining quality and safety.
Stay current on modern AI and data engineering patterns; recommend adoption only where it improves business outcomes and can be governed, secured, and supported.
Partner with the Director, Data Analytics & Enterprise Architecture to evolve data and AI engineering strategy, ensuring the team’s direction balances innovation with governance, security, reliability, and long-term maintainability.
Ensure alignment with organizational policies and procedures and applicable regulatory expectations; maintain awareness of security, privacy, and compliance implications of data and AI solutions.
Represent the engineering function in cross-functional discussions; contribute to planning, prioritization, and roadmap shaping to maximize organizational value from data and AI investments.
Promote data literacy and responsible AI usage across stakeholders through enablement, clarity of definitions, and consistent communication on how to use and interpret outputs.