Senior Software Engineer- AI & Data Platforms

First Electronic Bank•Salt Lake City, UT

About The Position

At First Electronic Bank (FEB), the Senior Software Engineer works on data engineering and AI platforms supporting the bank’s goal of making credit accessible to everyday Americans and businesses. The role independently leads high-impact data engineering workstreams from problem definition through measurable production outcomes, building reliable pipelines, trusted data models, and governed AI workflows while owning design, delivery, operational readiness, and results within Engineering standards. The position combines Python and SQL engineering with coding agents, retrieval systems, and targeted small-model experimentation. It evaluates architectures across approved cloud platforms for security, correctness, performance, recoverability, and cost, and helps establish reusable patterns for secure, controlled access to enterprise data. Representative work includes modernizing warehouse and lakehouse pipelines; integrating partner data through APIs, files, and secure shares; designing event-driven data flows; strengthening reconciliation and automated recovery; building permission-aware agents; and evaluating emerging defenses in isolated environments.

Requirements

At least 5 years of production data engineering experience, or equivalent scope, delivering reliable cloud-based solutions.
Demonstrated success independently leading ambiguous work through sustained production use while mentoring engineers and improving team practices.
Strong Python and advanced SQL skills, including automated testing, query-plan analysis, performance tuning, and transformation validation.
Production experience with Snowflake, Microsoft Fabric, Azure, AWS, or a comparable platform, including orchestration and data modeling.
Experience integrating APIs, files, databases, or data shares, including incremental processing, backfills, schema changes, reconciliation, and recovery.
Demonstrable use of coding agents to delegate meaningful work, manage context and handoffs, verify changes, and deliver tested results.
Hands-on experience building a retrieval, agent, or LLM-enabled workflow over real data; understanding of evaluation, prompt-injection risks, permission boundaries, and limitations.
Practical knowledge of CI/CD, infrastructure as code, least privilege, secrets management, observability, and troubleshooting.
Ability to translate ambiguous business needs into focused solutions and explain tradeoffs to non-technical users.
Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related field.

Nice To Haves

Advanced Snowflake engineering, including secure sharing, Snowpark, performance optimization, and cost governance.
Experience with Microsoft Fabric, Azure Data Factory, Synapse Analytics, Dataverse, Power BI semantic models, or warehouse-to-lakehouse modernization.
Experience with event streaming and enterprise message queues, including Azure Service Bus, Azure Event Hubs, Apache Kafka, IBM MQ, or comparable platforms; knowledge of partitioning, ordering, replay, idempotent consumers, backpressure, and failure recovery.
AWS data services, secure cross-cloud integration, infrastructure automation, or private-network architectures.
Production agent systems, MCP integrations, persistent state, evaluation, tracing, and recovery.
Agent security, sandboxing, adversarial evaluation, or deception engineering; familiarity with emerging AI security research.
Small-model fine-tuning, LoRA/PEFT, quantization, self-hosted inference, or reproducible model evaluation.
Experience in banking, fintech, payments, or another regulated environment.
Relevant certifications, or a master’s or doctoral degree in a related field.

Responsibilities

Clarify problems and success measures; design solutions within established architecture; manage dependencies and risks; and drive assigned workstreams through delivery, adoption, production support, troubleshooting, and improvement while following Change Management processes.
Build secure batch, streaming, and message-driven pipelines and reusable data services; evaluate compute placement, data movement, throughput, latency, delivery guarantees, recoverability, and operating cost.
Monitor cloud compute and storage spending, identify wasteful patterns, and right-size workloads using FinOps discipline.
Build idempotent, replayable pipelines with deduplication, schema-evolution handling, and support for late-arriving data.
Develop reconciliation, control-total, checkpoint, and bounded automated recovery tooling with human escalation, supporting Finance and Operations sign-off.
Develop curated datasets, documented models, and semantic layers for internal and partner-facing reporting; work with business users and BI on definitions, validation, lineage, and controlled access.
Delegate bounded tasks to approved coding agents, provide repository context, manage handoffs, and independently validate generated code and data while retaining human ownership of quality, security, and release decisions.
Build retrieval and tool-using agents over approved data and MCP servers, implementing permission-aware access, state management, evaluations, tracing, recovery, and approvals; treat retrieved content as untrusted.
Partner with Security to build and test defensive AI controls, including segmented agent trust zones, isolated execution, policy-enforced access, and auditable tool use.
Evaluate emerging AI security and adversarial testing techniques through controlled, authorized exercises in Security-provided sandbox environments and governance; produce evidence for go-live decisions and measure containment, detection, and effects on legitimate workflows.
Prototype small-model or machine-learning applications for classification, extraction, entity matching, and data-quality triage; compare reproducible evaluations with simpler approaches before recommending production use.
Own reliability, operational readiness, runbooks, and remediation within assigned workstreams; collaborate on CI/CD, identity, networking, secrets, observability, capacity, incidents, and costs.
Produce audit evidence, support control testing, maintain rollback plans, and ensure appropriate human approvals.
Mentor through design and code reviews and convert proven approaches into reusable components, playbooks, and validated engineering and security patterns.