Backend Integrations Engineer

Build-A-Bear Workshop; St. Louis (MO); Hybrid; USD 110,000 - 160,000; Full time

Benefits offered by this job

Hybrid work schedule

Ergonomic office environment

Job summary

Build-A-Bear Workshop seeks a Backend Integrations Engineer to design, build, and maintain data pipelines powering enterprise decisions. This role spans cloud platforms, digital systems, and analytics tools to ensure reliable, scalable data for reporting and AI initiatives.

You will help build the foundation that powers guest experiences and data-driven decisions across the organization, collaborating to define data requirements and governance while staying current on analytics, AI, and

Qualifications

• 5+ years of experience in data engineering, data warehousing, or analytics platform development.
• Bachelor’s degree in computer science, information systems, data engineering, or related field.
• Expert knowledge of T-SQL and PLSQL.
• Intermediate knowledge of Python and PySpark.
• Intermediate knowledge of OOP principles and proficiency with one or more languages (such as C#, Java).
• Expert with cloud-based data ecosystems, ideally Microsoft Azure (Data Factory, Synapse, Databricks, or Data Lake).
• Skilled understanding of ETL/ELT design patterns, data modeling, and schema optimization.
• Proficient knowledge of API integrations, such as REST, SOAP.
• Basic knowledge of BI tools such as Power BI, Tableau, or Looker.

Responsibilities

• Design, build, and maintain scalable data pipelines and integrations across multiple enterprise systems.
• Develop and maintain data models, ETL/ELT workflows, integrations, and orchestration logic within cloud data environments and vendors.
• Collaborate to define data requirements and ensure alignment between business needs and technical solutions.
• Implement data quality validation, monitoring, and governance processes.
• Partner with IT Security and Privacy teams to enforce data access controls and compliance standards.
• Support development of reusable frameworks for data ingestion, transformation, and storage.
• Document technical designs, data dictionaries, and workflows for transparency and maintainability.
• Stay current on development best practices, tools, and emerging trends in analytics, AI, and automation.

Skills

T-SQL

PL/SQL

Python

PySpark

OOP (C#, Java)

Azure (Data Factory, Synapse, Databr

ETL/ELT design

Data modeling

REST API integrations

BI tools

Education

Bachelor’s degree in computer science, information systems, data engineering, or related field

Tools

Azure Data Factory

Azure Synapse

Azure Databricks

Data Lake

Job description

The Backend Integrations 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.

At Build-A-Bear, our data fuels creativity, efficiency, and innovation, connecting millions of guests to memorable experiences. The Backend Integrations Engineer will help build the foundation that powers these experiences.

Responsibilities

• Design, build, and maintain scalable data pipelines and integrations across multiple enterprise systems.
• Develop and maintain data models, ETL/ELT workflows, integrations, and orchestration logic within cloud data environments and vendors.
• Collaborate to define data requirements and ensure alignment between business needs and technical solutions.
• Implement data quality validation, monitoring, and governance processes.
• Partner with IT Security and Privacy teams to enforce data access controls and compliance standards.
• Support development of reusable frameworks for data ingestion, transformation, and storage.
• Document technical designs, data dictionaries, and workflows for transparency and maintainability.
• Stay current on development best practices, tools, and emerging trends in analytics, AI, and automation.

Required Qualifications

• 5+ years of experience in data engineering, data warehousing, or analytics platform development
• Bachelor’s degree in computer science, information systems, data engineering, or related field
• Expert knowledge of T-SQL and PLSQL
• Intermediate knowledge of Python and PySpark
• Intermediate knowledge of OOP principles and proficiency with one or more languages (such as C#, Java, etc.)
• Expert with cloud-based data ecosystems, ideally Microsoft Azure (Data Factory, Synapse, Databricks, or Data Lake)
• Skilled understanding of ETL/ELT design patterns, data modeling, and schema optimization
• Proficient knowledge of API integrations, such as REST, SOAP
• Basic knowledge of BI tools such as Power PI, Tableau, or Looker
• Excellent communication and critical thinking skills, with the ability to translate technical concepts for business partners

Preferred Qualifications

• Experience with Microsoft Dynamics 365, Salesforce, or similar enterprise data sources
• Understanding of data governance, lineage, and master data management frameworks
• Exposure to DevOps principles, CI/CD pipelines, and version control
• Familiarity with Agile or Scrum methodologies
• Microsoft Certified: Azure Data Engineer Associate or similar certification

Behavioral Traits For Success

• Enjoys being recognized for the quality of their work
• Willingness to work within established standards, guidelines, and procedures
• Develops strong job knowledge and competency
• Strong commitment to tasks being completed correctly and on time
• Thrives in a structured environment
• Happy accomplishing work as an individual
• Can work harmoniously with others
• Communication is factual, polite, and professional

Working Environment

• Typical office environment with climate control and sufficient lighting, ergonomic desk/chairs
• Hybrid work schedule

Your Performance Will Be Measured On

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.

• Decision-making, judgment, and execution
• System reliability
• Data throughput and performance metrics
• Reduction of data defects
• Accuracy and efficiency
• Compliance adherence
• Data integrity
• Audit readiness
• Stakeholder Feedback