Data Engineer

The Detroit Pistons Business Strategy & Analytics team is seeking a Data Engineer to develop, maintain, and optimize a Microsoft-based data infrastructure. This role supports the existing SQL Server data warehouse while contributing to a transition toward a lakehouse approach using Microsoft Fabric.

Responsibilities

• Develop, maintain, and optimize data pipelines, data models, database objects, and integration processes within a Microsoft-based data environment.
• Support the transition from a traditional data warehouse structure to a modern data lakehouse methodology using Microsoft Fabric.
• Build scalable ETL/ELT processes to ingest, transform, validate, and deliver data for reporting, analytics, and business intelligence use cases.
• Support the design and implementation of Microsoft Fabric components, including OneLake, Lakehouse, Warehouse, Data Factory pipelines, Dataflows Gen2, notebooks, and Power BI integration.
• Support the Senior Director of Data Strategy & Reporting with implementing data governance standards, data quality processes, and enterprise data best practices.
• Develop and maintain data validation rules, reconciliation processes, and data quality checks across critical business datasets.
• Ensure data accuracy, consistency, integrity, privacy, and reliability across source systems, data warehouse objects, lakehouse assets, and reporting outputs.
• Prepare reliable, well-documented, and governed data assets for reporting, analytics, automation, and AI applications.
• Support Power BI datasets, semantic models, dataflows, and reporting infrastructure as needed.
• Partner with analysts and business stakeholders to ensure data models align with business requirements, reporting standards, and performance expectations.
• Integrate data from multiple internal and external systems into the organization’s data warehouse, data lakehouse, and analytics environment.
• Work with APIs, flat files, relational databases, SaaS platforms, and cloud-based systems to ensure reliable data ingestion and availability.
• Monitor pipeline health, investigate failures, and implement long-term solutions that improve reliability and reduce operational risk.
• Maintain clear technical documentation covering integrations, refresh schedules, dependencies, data transformations, and business logic.

Requirements

• Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field (preferred).
• Minimum of 4 years of professional experience in data engineering, database development, business intelligence, data warehousing, or a related data role.
• Experience working in a Microsoft data environment is required.
• Strong proficiency with Microsoft SQL Server and MS SQL data warehouse environments.
• Advanced T-SQL skills, including stored procedures, views, joins, indexing, query optimization, and data modeling.
• Experience building ETL/ELT pipelines from multiple structured and semi-structured data sources.
• Strong understanding of data warehouse concepts, dimensional modeling, star schemas, data marts, and modern lakehouse methodologies.
• Familiarity with Microsoft Fabric, Azure Data Factory, Power BI, Azure SQL, Azure Data Lake, Synapse, or related Microsoft data tools.
• Experience with Python, PySpark, Spark SQL, or other modern data engineering languages and frameworks.
• Familiarity with preparing data for AI applications, including data quality, metadata, and secure access.
• Ability to translate business needs into scalable data engineering solutions.
• Ability to manage multiple priorities and deliver high-quality work in a fast-paced environment.

Location and Experience

Location: Detroit, MI (onsite)

Minimum Experience: 4 years

Relevant Technologies

Microsoft SQL Server, MS SQL data warehouse, T-SQL, ETL/ELT, Microsoft Fabric, OneLake, Lakehouse, Warehouse, Data Factory pipelines, Dataflows Gen2, notebooks, Power BI, Power BI datasets, semantic models, dataflows, APIs, flat files, relational databases, SaaS platforms, Azure Data Factory, Azure SQL, Azure Data Lake, Synapse, Python, PySpark, Spark SQL