Data Engineer

Novogradac • Cleveland, OH

1d • Hybrid

About The Position

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.

Requirements

• 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.
• Ability to collaborate effectively with business intelligence, systems, and IT teams.
• Ability to effectively manage and prioritize a fast-paced workload while meeting deadlines and adapting to changing business needs.
• Ability to work independently and as part of a team, maintaining a collaborative and proactive approach to assigned responsibilities.
• Bachelor's degree in Computer Science, Information Systems Management, Data Science, Data Analytics, or a related field and at least 3 years of experience in data engineering or analytics engineering roles, including hands-on development of ETL/ELT pipelines.
• Demonstrated proficiency in SQL and Python, as well as experience with cloud data platforms and modern analytics tooling, is required.

Nice To Haves

• Experience with Microsoft Azure data services (e.g. Azure Data Factory, Databricks, Synapse/Fabric, Snowflake).
• Experience implementing medallion architectures and building semantic models in Power BI/Microsoft Fabric, including star schemas and Direct Lake.
• Familiarity with DevOps or CI/CD practices for data pipelines.
• Exposure to BI tools such as Power BI.
• Azure certifications (e.g., Azure Data Engineer Associate, Fabric Analytics Engineer Associate) are a plus.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.

Benefits

• Increased number of paid holidays per year
• Competitive salaries with continuous review of market conditions
• Flexible working hours and work arrangements
• Remote and hybrid opportunities
• Inclusive workplace, providing strong professional growth and development opportunities
• Strong growth opportunities
• Competitive benefits package
• 401(k) package with firm profit-sharing
• Strong emphasis on quality work-life integration
• Dress for your day policy
• Resources of a national firm
• Opportunities to engage with our active Employee Resource Groups (ERGs), affinity groups, and advance your career within a supportive, inclusive environment
• medical
• dental
• vision
• paid time off
• life/disability insurance
• commuter flex accounts
• 401(k)

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level