Data Analytics Engineer

D’Addario & Company is the largest manufacturer and distributor of musical instrument accessories in the world. As a US based manufacturing company, we pride ourselves on high automation machinery and innovative technology, as well as our commitment to environmentally sustainable practices. Through our work with the D'Addario Foundation we are committed to helping make music education accessible and ensuring hundreds of thousands of students can participate in music-making programs worldwide, regardless of instrumentation, genre, or life circumstance. Most importantly, we pride ourselves on our diverse team of individuals who commit to the embodiment of our core values of curiosity, passion, candor, family and responsibility and translate them into action every day.

The Business Intelligence team is looking for a hands-on Data Analytics Engineer to help build, transform, and optimize D’Addario’s global data infrastructure. In this role, you’ll design and maintain production-grade data pipelines while modeling the clean, well-tested, and documented datasets that power reporting and self-service analytics across the company. You’ll work closely with the Global Director of BI and our analysts to turn raw data into reliable, business-ready data products.

This is an ideal opportunity for an engineer who thrives at bringing data into order , enjoys solving complex data challenges, and is passionate about enabling organizational data intelligence.

Responsibilities

• Build & Optimize Pipelines: Design, implement, and maintain robust, high-performance ELT/ETL data pipelines within Microsoft Fabric and our broader data environment.
• Data Integration: Connect and harmonize new data sources—including ERP, e-commerce platforms, and external APIs—into a centralized data platform.
• Analytics Data Modeling: Transform raw data into clean, well-structured dimensional models, data marts, and reusable datasets (star schemas) that are analysis-ready for reporting and self-service BI.
• Semantic Layer & Metrics: Build and maintain semantic models and standardized metric definitions in Power BI and Microsoft Fabric so the business works from a single, trusted source of truth.
• Data Quality & Testing: Implement automated testing, validation, and monitoring to ensure pipelines and datasets are accurate , reliable, and well-documented.
• Collaboration: Partner with analysts and stakeholders across Sales, Marketing, Operations, and Product to translate business requirements into reliable data products.
• Continuous Improvement: Follow and help improve team standards for coding, documentation, version control, and DataOps across our data engineering and analytics workflows.

Qualifications

• 3+ years of hands-on experience as a Data Engineer, Analytics Engineer, or equivalent
• Experience building and maintaining production-grade data pipelines and analytics data models
• Proficiency in Python, PySpark , and SQL
• Bachelor's degree in Computer Science , Engineering, Data Science, or related field (or equivalent professional experience)
• Preferred experience with Microsoft Fabric, Azure Synapse, or Azure Data Lake
• Preferred experience implementing DataOps best practices and building transformation models with dbt or similar frameworks
• Familiarity with API integrations and third-party data ingestion
• Knowledge of data governance and data quality frameworks
• Musician or passion for music a plus

Knowledge, skills, & abilities:

• Strong programming in Python and PySpark for data processing and transformation
• Advanced SQL and dimensional data modeling (e.g., star schema / Kimball) for analytical performance and scalability
• Experience building and maintaining ELT/ETL pipelines and transformation layers, including automated testing and validation of analysis-ready datasets
• Strong understanding of cloud data platforms (Azure preferred)
• Excellent communication skills with the ability to simplify complex technical concepts
• Familiarity with semantic modeling and BI tools such as Power BI and Microsoft Fabric
• Self-directed, highly organized, and comfortable operating in a fast-paced, evolving environment
• Passion for innovation and leveraging data to create business impact