Data Solutions Engineer
Job Description
Responsibilities:
• Guide and support data engineering and BI team members by setting direction, encouraging growth, and promoting strong cross-functional collaboration.
• Translate business priorities into measurable delivery goals, establish performance indicators, and monitor progress against expected outcomes.
• Architect, develop, and maintain robust data pipelines that move and transform information across internal platforms and third-party sources.
• Oversee ETL operations to ensure data is accurate, timely, and dependable for downstream reporting and analytics needs.
• Build and refine data models and warehouse structures that enable reporting, advanced analytics, and future predictive or machine learning use cases.
• Apply sound engineering practices such as automation, source control, and repeatable development standards to improve data platform quality and efficiency.
• Lead the creation and enhancement of Power BI and SSRS reporting solutions with a focus on usability, consistency, and alignment with business objectives.
• Partner with business stakeholders to gather requirements, convert them into technical designs, and deliver scalable BI and data solutions.
• Define visualization standards, reusable datasets, and semantic layers that support self-service reporting across the organization.
• Proven experience in data engineering with strong knowledge of ETL design, development, and support.
• Experience with Shopify is required.
• Retail industry experience strongly preferred.
• Hands-on expertise with DataStage ETL in enterprise data integration environments.
• Strong background building reporting and analytics solutions using Power BI, including semantic models and reusable datasets.
• Experience with Microsoft SSRS for operational or business reporting delivery.
• Solid understanding of data warehousing concepts, dimensional modeling, and scalable data architecture practices.
• Ability to lead or mentor technical teams while working effectively with business partners and cross-functional stakeholders.
• Familiarity with version control practices such as Git and a disciplined approach to development standards.
• Exposure to machine learning or AI-related data use cases is a plus.
