Senior Engineer, Data Engineering

National Debt Relief, LLC.•,

1d•$144,000 - $165,500•Remote

About The Position

National Debt Relief seeks a Senior Data Engineer to shape the architecture, reliability, and evolution of its Snowflake-based data platform, which supports analytics and business decision-making. Reporting to the Senior Data Engineering Manager, the role owns one or more data domains spanning ingestion, orchestration, transformation, and analytics-ready data models. The engineering team uses AI coding agents as a core part of its development and maintenance workflow. The successful candidate will bring exceptional SQL and data modeling skills, deep expertise in one or two areas of the modern data stack, and strong proficiency across the rest. The role involves turning ambiguous problems into durable solutions, owning production outcomes for critical data, establishing reusable engineering patterns, and raising technical standards through design, review, and mentorship.

Requirements

At least 7 years of experience in data engineering, analytics engineering, or data warehouse development, including ownership of production data systems in a cloud environment.
Bachelor's degree in a relevant technical field or equivalent practical experience.
Experience using Python to build or maintain integrations with APIs and third-party data sources.
Experience implementing CI/CD workflows, version control, automated testing, and observability for production data pipelines.
Expert SQL and data modeling across a domain, including grain, keys, conformed dimensions, incremental strategies, layered architecture, and semantic implications.
Strong working proficiency with dbt, Dagster, and Snowflake, plus proficiency in Python and infrastructure-as-code; expert-level depth in one or two areas of the stack.
Advanced proficiency with AI coding agents, including identifying likely failure modes, providing context and constraints, verifying generated work, and creating reusable workflows or guardrails.
Strong architecture and systems thinking covering dependencies, failure modes, scalability, security, maintainability, and cost.
Systematic root-cause analysis across platform layers, identifying subtle or systemic problems and driving durable corrective action.
Ability to work autonomously from desired outcomes, make progress with incomplete information, and bring ambiguous work to closure.
Clear communication of complex technical topics to engineers, analysts, business stakeholders, and leadership, with experience mentoring engineers.
Business acumen and prioritization judgment, connecting technical decisions to business outcomes, risk, and cost.
Computer competency and ability to work with a computer and navigate multiple computer systems.
Ability to prioritize multiple tasks and projects simultaneously and work in a fast-paced, high-volume setting.
Exceptional written and verbal communication skills.
Punctuality and consistent attendance; ability to meet monthly performance expectations.
Ability to remain calm and professional during difficult discussions and take constructive feedback.
Available for a full-time position.

Responsibilities

Own the architecture and reliability of one or more data domains end to end, establishing reusable patterns and conventions.
Define and deliver ambiguous, cross-cutting solutions, making and explaining tradeoffs related to business outcomes, reliability, and cost.
Identify platform architecture, reliability, and maintainability improvements; develop well-scoped initiatives and influence technical priorities.
Develop and maintain Python-based ingestion pipelines for APIs and third-party systems when custom pipelines are required.
Design and build data pipelines and workflows with dbt, Python, Dagster, and Snowflake; document significant designs and consider failure modes, scalability, cost, and maintainability.
Use AI coding agents in daily work and develop reusable workflows, verification practices, and guardrails for the team.
Establish safeguards for data quality and trust through testing, data contracts, monitoring, reconciliation, and alerting.
Own domain reliability, including SLAs, alerting, runbooks, morning load failures during Eastern Time working hours, on-call rotation, complex incident response, and post-incident reviews.
Manage Snowflake infrastructure and access controls for sensitive data using infrastructure-as-code and applicable security, governance, and privacy standards.
Optimize Snowflake performance and compute costs through query design, warehouse tuning, architecture decisions, and resource monitoring.
Develop other engineers through design and code review, pairing, mentorship, documentation, and technical guidance.