Data Engineer

It's fun to work in a company where people truly BELIEVE in what they're doing!
We're committed to bringing passion and customer focus to the business.

Dare to bring your unique perspective?

At Novogradac, we value individuals who bring unique perspectives and bold ideas. If you’re eager to contribute to an inclusive, innovative environment where your personal and professional growth is encouraged and supported, we invite you to explore this exciting opportunity with us.

Position Summary:

The Data Engineer is responsible for building, operating, and optimizing the organization’s analytical data pipelines and platforms. This position develops reliable, scalable, and performant data assets to support enterprise analytics and reporting. The Data Engineer designs, develops, maintains, and optimizes ETL/ELT pipelines, data warehouse structures, and semantic models through the use of SQL, Python, and related technologies.

The firm has one available position in either Austin, TX, Bellevue, WA, Long Beach, CA, Portland, OR, Dover, OH, Atlanta, GA, Cleveland, OH, or Fort Lauderdale, FL.

Your Contributions and Responsibilities

Data Pipeline & Platform Engineering

• Design, develop, and maintain ETL/ELT pipelines from source systems into analytical platforms, writing production-grade SQL and Python.

• Implement transformations, validations, and aggregations to support analytics and reporting

• Ensure data pipelines are resilient, observable, and performant.

• Monitor, troubleshoot, and remediate data pipeline failures and performance issues.

Database Performance & Optimization

• Diagnose and resolve slow-running queries and optimize SQL syntax for efficiency and readability.

• Design, implement, and tune indexing strategies to improve query performance.

• Implement referential integrity constraints (e.g., foreign keys) where appropriate to the platform and data model, complementing pipeline-based data-quality checks.

• Assist with memory, resource, and configuration tuning of data platforms in coordination with IT.

Data Warehouse & Analytics Enablement

• Implement and optimize data warehouse schemas and structures in accordance with the organization’s architectural direction.

• Implement, maintain, and optimize data across the layers, and provide practical feedback to inform layer design and promotion logic.

• Build, optimize, and maintain physical semantic models (e.g., Power BI/Fabric star schemas, Direct Lake datasets) that implement the business definitions, metrics, and relationships specified.

• Partner with others to operationalize architectural designs and analytical data models.

• Support BI Analysts by ensuring data availability, freshness, and usability.

• Manage dependencies and sequencing across multiple source systems.

Quality, Reliability, & Operations

• Implement data quality checks, reconciliation controls, and error handling.

• Ensure data processing adheres to security, privacy, and access requirements.

• Maintain documentation for pipelines, data flows, semantic models, and operational procedures.

• Participate in incident response and root-cause analysis for data issues.

Collaboration & Continuous Improvement

• Collaborate with systems product managers to understand system changes affecting data.

• Partner with IT on infrastructure, access, and platform considerations.

• Evaluate and recommend improvements to data tooling, patterns, and performance.

• Contribute to engineering best practices, coding standards, and reusable components.

Your Background and Skills

• Strong knowledge of data engineering principles, data integration processes, and analytical data warehouse concepts.

• Knowledge of data quality, data governance, security, and privacy practices applicable to analytical data environments.

• Strong problem-solving skills and attention to operational detail, including troubleshooting and root-cause analysis.

• Ability to monitor, troubleshoot, and optimize data pipelines, databases, and related data platforms to support operational reliability and performance.

• Ability to evaluate processes and recommend improvements that enhance scalability, efficiency, and data reliability.

• Strong verbal and written communication skills with the ability to effectively communicate technical information to technical and non-technical audiences.