<p>The Data Engineer helps design, build, and maintain pipelines and platforms that power our data-driven decisions across the enterprise. This role will work across cloud platforms, digital systems, and analytics tools to ensure our data is reliable, scalable, and ready for everything from business reporting to advanced AI applications.</p> <p>At Build-A-Bear our data fuels creativity, efficiency, and innovation, connecting millions of guests to memorable experiences. The Data Engineer will help build the foundation that powers these experiences.</p> <p>Responsibilities:</p> <ul> <li>Design, build, and maintain scalable data pipelines and integrations across multiple enterprise systems. </li><li>Develop and maintain data models, ETL/ELT workflows, and orchestration logic within cloud data environments. </li><li>Collaborate to define data requirements and ensure alignment between business needs and technical solutions. </li><li>Implement data quality validation, monitoring, and governance processes. </li><li>Optimize performance and efficiency of existing data processes, enabling faster insights and reduced latency. </li><li>Partner with IT Security and Privacy teams to enforce data access controls and compliance standards. </li><li>Support development of reusable frameworks for data ingestion, transformation, and storage. </li><li>Document technical designs, data dictionaries, and workflows for transparency and maintainability. </li><li>Stay current on data engineering best practices, tools, and emerging trends in analytics, AI, and automation. </li></ul> <p>Required Qualifications:</p> <ul> <li>5+ years of experience in data engineering, data warehousing, or analytics platform development </li><li>Bachelor's degree in computer science, information systems, data engineering, or related field </li><li>Skilled in SQL and familiarity with Python, Spark, or other scripting languages for data manipulation </li><li>Expert with cloud-based data ecosystems, ideally Microsoft Azure (Data Factory, Synapse, Databricks, or Data Lake) </li><li>Skilled understanding of ETL/ELT design patterns, data modeling, and schema optimization </li><li>Proficient knowledge of API integrations and real-time data streaming </li><li>Basic knowledge of BI tools such as Power PI, Tableau, or Looker </li><li>Excellent communication and critical thinking skills, with the ability to translate technical concepts for business partners </li></ul> <p>Preferred Qualifications:</p> <ul> <li>Experience with Microsoft Dynamics 365, Salesforce, or similar enterprise data sources </li><li>Understanding of data governance, lineage, and master data management frameworks </li><li>Exposure to DevOps principles, CI/CD pipelines, and version control </li><li>Familiarity with Agile or Scrum methodologies </li><li>Microsoft Certified: Azure Data Engineer Associate or similar certification </li></ul> <p>Behavioral Traits for Success:</p> <ul> <li>Enjoys being recognized for the quality of their work </li><li>Willingness to work within established standards, guidelines, and procedures </li><li>Develops strong job knowledge and competency </li><li>Strong commitment to tasks being completed correctly and on time </li><li>Thrives in a structured environment </li><li>Happy accomplishing work as an individual </li><li>Can work harmoniously with others </li><li>Communication is factual, polite, and professional </li></ul> <p>Working Environment:</p> <ul> <li>Typical office environment with climate control and sufficient lighting, ergonomic desk/chairs </li><li>Hybrid work schedule </li></ul> <p>Your Performance Will Be Measured On:</p> <p>Your performance will be measured by your ability to achieve annual department objectives and corporate goals which include but are not limited to the following.</p> <ul> <li>Decision-making, judgment, and execution </li><li>System reliability </li><li>Data throughput and performance metrics </li><li>Reduction of data defects </li><li>Accuracy and efficiency </li><li>Compliance adherence </li><li>Data integrity </li><li>Audit readiness </li><li>Stakeholder Feedback </li></ul>