Engineer, Data Engineering

National Debt Relief, LLC.•,

1d•$125,000 - $144,000•Remote

About The Position

National Debt Relief is seeking a Data Engineer to join its Data Engineering team and build, operate, and improve the data platform that supports business analytics and decision-making. Reporting to the Senior Data Engineering Manager, the role works across a Snowflake-based platform, including ingestion, orchestration, transformation, and analytics-ready data models. The team uses AI coding agents as a core part of its development and maintenance workflows. The engineer is expected to exercise sound technical judgment, understand the systems and data they support, and take ownership of production outcomes. The role calls for strong SQL and data modeling fundamentals, depth in at least one area of the modern data stack, and the ability to work across the broader platform. It is suited to a curious, proactive engineer focused on reliable, maintainable data solutions.

Requirements

At least 4 years of experience in data engineering, analytics engineering, or data warehouse development.
Bachelor’s degree in a relevant technical field or equivalent practical experience.
Experience using Python to retrieve and integrate data from APIs.
Experience with CI/CD workflows, version control, and automated testing for data pipelines.
Strong SQL and data modeling skills, including defining appropriate grain and keys, designing incremental strategies, organizing maintainable model layers, and balancing simplicity, performance, and usability.
Working proficiency across dbt, Dagster, and Snowflake, with familiarity with Python and infrastructure-as-code; strong depth in at least one area of the stack and judgment about when to seek additional expertise.
Proficiency using AI coding agents, including providing context and constraints, breaking work into effective tasks, and critically reviewing generated code and outputs.
Ability to diagnose data and pipeline issues across platform layers, identify root causes, and distinguish isolated failures from systemic problems.
Ownership mindset, including proactively addressing problems and considering work complete when the data is correct, not merely when a pull request merges.
Ability to develop a strong understanding of supported pipelines and data domains, including dependencies, business logic, failure modes, and downstream consumers.
Clear, timely, and proactive written communication, including raising blockers early.
Business acumen and ability to translate business needs into technical solutions.
Ability to prioritize multiple projects effectively while keeping progress visible.
Computer competency and ability to work with a computer and navigate multiple systems while multitasking.
Exceptional written and verbal communication skills.
Punctuality and consistent attendance; ability to meet monthly performance expectations.
Ability to work in a fast-paced, high-volume setting, remain calm and professional during difficult discussions, and take constructive feedback.
Available for a full-time position.

Nice To Haves

Experience in financial services or related industries.
Experience with Terraform or similar infrastructure-as-code tooling.
Experience with semantic layers, metric definitions, or BI tools.

Responsibilities

Own data engineering projects end to end across ingestion, orchestration, transformation, and the analytics layer, from defining scope through verified delivery, including loosely scoped projects.
Develop and maintain Python-based ingestion pipelines when custom pipelines are required, integrating data from APIs and third-party systems.
Design and build data pipelines and workflows using dbt, Python, Dagster, and Snowflake, making sound modeling and design decisions within the established architecture.
Use AI coding agents in daily work, directing them effectively and reviewing and verifying all shipped work.
Ensure data quality and trust through testing, data contracts, and automated alerting.
Monitor and support production pipelines, including morning load failures during Eastern Time working hours, on-call rotation, and incident triage.
Manage Snowflake infrastructure and access controls for sensitive data through infrastructure-as-code, following security, governance, and privacy standards.
Optimize Snowflake performance and manage compute costs through efficient query design, warehouse tuning, and resource utilization monitoring.
Identify opportunities to improve the platform, define the work, evaluate requirements critically, and escalate out-of-scope decisions with recommended solutions.
Collaborate with engineers, analysts, and business stakeholders through code review, documentation, and team discussions.