Data Engineer

NAC Architecture•Seattle, WA

2d•$125,000 - $150,000•Onsite

About The Position

NAC is looking for an experienced Data Engineer to join our delivery team, with a specific focus on designing, building, and optimizing data solutions on Microsoft Azure and Microsoft Fabric. This role is critical to helping NAC unlock the full value of our data assets, supporting advanced analytics, reporting, and digital transformation across the firm. The ideal candidate is confident working with a modern data stack, automating data pipelines, and delivering reliable, scalable, high-quality data solutions in enterprise environments. The day-to-day work ranges from ingesting data out of firm business systems into a governed lakehouse to tuning the models and datasets that our reporting depends on, with an emphasis on solutions that keep working without constant attention. This position reports to our Data Leader and will work closely with IT leadership and the data team, as well as resources supporting data architecture and data governance.

Requirements

Bachelor's degree in computer science, Data Engineering, Information Systems, or a related field preferred (or equivalent experience).
At least 3 years of experience as a Data Engineer, ideally in professional services or large enterprise environments.
Demonstrable expertise in building data pipelines and cloud data solutions on Azure.
Experience with Microsoft Fabric is strongly preferred.
Strong hands-on experience with Microsoft Azure data services (e.g. Data Factory, Synapse Analytics, Data Lake, Databricks).
Direct experience delivering solutions using Microsoft Fabric.
Advanced SQL skills and experience with data modeling, transformation, and integration.
Knowledge of Python or other scripting languages is an advantage.
Familiarity with data governance, data quality, and compliance frameworks.
Certification in Microsoft Azure data engineering or Microsoft Fabric is a plus.
Excellent communication skills, able to translate technical solutions into business value.
Ability to work independently and collaboratively in a fast-paced, client-facing environment.
Comfortable working across teams and explaining technical concepts to non-technical audiences.
Microsoft Fabric and the Azure data platform (Data Factory, Synapse Analytics, Data Lake, Databricks).
Power BI and Microsoft 365 (Teams, SharePoint, Excel).
Source control and deployment tooling (e.g., Git, Azure DevOps).
Proactive, resourceful, and adaptable.
Committed to continuous improvement, automation, and reducing manual effort over time.
Collaborative, service-oriented mindset with the ability to work across teams.
Strong analytical, organizational, and problem-solving abilities.
High degree of empathy and customer orientation.
Ability to meet deadlines, manage priorities, and deliver initiatives successfully.

Nice To Haves

Certification in Microsoft Azure data engineering or Microsoft Fabric is a plus.

Responsibilities

Design, build, and maintain scalable data pipelines and data integration workflows using Microsoft Azure services such as Data Factory, Synapse Analytics, Azure Databricks, and related technologies.
Develop and maintain ETL/ELT processes that support business reporting, analytics, and machine learning.
Build ingestion patterns for a range of sources, including business applications, APIs, relational databases, and file-based feeds.
Implement scheduling, orchestration, automation, and error handling so recurring data loads run reliably with minimal manual intervention.
Implement and support Microsoft Fabric solutions, including dataflows, lakehouses, data warehouses, and real-time analytics features.
Organize data in OneLake using a layered approach that keeps raw, enriched, and curated data clearly separated and traceable.
Develop and maintain curated datasets and semantic models that support Power BI reporting and self-service analysis.
Monitor capacity, refresh schedules, and job performance, and tune them as adoption and data volumes grow.
Optimize data architectures for performance, reliability, and cost efficiency in cloud environments.
Apply dimensional modeling and other established modeling patterns so data is understandable, reusable, and consistent across reporting.
Write and tune advanced SQL, along with Python or PySpark notebooks, for complex transformation work.
Monitor, troubleshoot, and improve data workflows, resolving failures and performance bottlenecks at the source rather than working around them.
Ensure high-quality, secure, and compliant data management practices in line with firm policy, client contractual obligations, and applicable data protection regulations.
Implement data quality rules, validation, and monitoring so issues are caught before they reach reporting or decision-making.
Apply access controls, sensitivity handling, and lineage practices in partnership with the resources supporting data governance.
Collaborate with data architects, analysts, and business stakeholders to gather requirements and deliver data solutions aligned with business goals.
Support the migration and modernization of legacy data systems into Azure and Microsoft Fabric environments.
Translate technical options and trade-offs into terms business stakeholders can act on.
Produce and maintain technical documentation for data pipelines, architecture, and engineering best practices.
Follow source control and deployment practices, including Git-based development and structured promotion across environments.