• Design, build, and maintain scalable data pipelines across diverse environments • Integrate data from multiple internal and external sources into data warehouses or data lakes • Collaborate closely with Data Scientists, ML Engineers, and Developers to ensure data quality, structure, and availability • Monitor and improve data integrity, performance, and reliability • Build and optimize database schemas, data models, and documentation • Implement data governance, security best practices, and compliance standards

Role Description The Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and modern data platform components that support enterprise analytics, reporting, and operational data needs. This role plays a key part in evolving the organization’s data architecture from legacy application databases to a modern cloud-based data warehouse built on a medallion architecture. The position requires strong technical expertise in SQL Server, cloud data platforms, and modern ELT practices while working closely with analytics, application development, and business teams to deliver reliable, well-governed data assets. The Data Engineer contributes to the design and implementation of a high-performance data ecosystem that supports advanced analytics, business intelligence, and data-driven decision making. How You’ll Drive Success - Data Pipeline Engineering - Design, develop, and maintain scalable data pipelines that ingest and transform data from enterprise platforms including NetSuite, Salesforce, HubSpot, and internally developed applications. - Develop efficient ELT workflows using modern integration tools such as Fivetran, Integrate.io, Azure Data Factory, or similar technologies to automate data ingestion and transformation. - Build and optimize SQL-based transformations and orchestration processes that support reliable and efficient movement of data across the organization’s data platform. - Data Warehouse Architecture and Development - Design and implement dimensional data models and curated datasets within a modern medallion architecture data warehouse environment. - Develop and maintain data layers supporting development, test, and production environments across cloud data platforms including Azure Data Lake, Snowflake, or equivalent platforms. - Implement scalable data structures and transformation frameworks using SQL, dbt, or similar tools to support high-performance analytics workloads. - Analytics and Business Intelligence Enablement - Develop and maintain analytical models including SQL Server Analysis Services (SSAS) cubes and semantic layers supporting enterprise reporting and analytical applications. - Collaborate with analytics and business intelligence teams to deliver optimized data models and datasets for visualization tools including Power BI and Tableau. - Ensure the accuracy, integrity, and availability of analytical data assets used across reporting and business intelligence environments. - Data DevOps, Governance, and Platform Operations - Implement and support database and data pipeline deployment processes using Visual Studio, Azure DevOps, Azure Git repositories, and Red Gate SQL Change Automation. - Monitor, troubleshoot, and optimize data platform performance, data pipeline reliability, and data quality across the enterprise data ecosystem. - Maintain clear documentation of data models, pipelines, architecture, and operational procedures to support maintainability and knowledge sharing. - Performs other duties as assigned. Qualifications - Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Data Science, or a related technical discipline. Requirements - 4 - 5 years of experience in data engineering, database development, or enterprise data warehousing environments. - Demonstrated experience building and maintaining data pipelines and large-scale data integration processes. - Experience working with SQL Server, relational database technologies, and modern cloud data platforms. - Experience integrating and modeling data from SaaS business applications such as Salesforce, NetSuite, or HubSpot. - Experience supporting analytics platforms and business intelligence tools such as SSAS, Power BI, or Tableau. Preferred Skills - Strong SQL development and data modeling skills with the ability to analyze complex data structures and design scalable solutions. - Ability to work within a structured development lifecycle utilizing version control, CI/CD pipelines, and DevOps practices. - Ability to collaborate effectively with software engineers, analysts, and business stakeholders to deliver reliable and well-governed data solutions. - Proficiency with Microsoft Office (Word, Excel and PowerPoint) as well as calculator, telephone systems, company network and familiarity with CRM tools. - Proven time management skills and a track record of working in a team environment successfully supporting others. - Well-organized, attention to detail and able to multitask. - Strong communication skills, both written and verbal, with the ability to provide accurate and timely information in a succinct and easy to understand manner. - Problem solving and analysis skills.

• Design and implement scalable and reliable data warehouse architecture, including data models, storage strategies, and processing layers. • Develop and maintain a robust role-based access control (RBAC) model to ensure secure and appropriate access to data across the organization. • Design, implement, and maintain inbound and outbound data pipelines, ensuring reliability, observability, and data integrity. • Optimize SQL queries, data models, and compute/storage resource usage to ensure cost-efficiency and high performance of the data platform. • Implement and enforce data protection and security best practices, including handling of sensitive and regulated data. • Work closely with analytics and business teams to understand their needs and deliver efficient, scalable, and reliable data solutions. • Improve and automate data governance practices, including data quality controls, metadata management, lineage, and compliance-related processes. • Monitor and troubleshoot data pipelines and platform issues, ensuring stability and timely resolution of incidents. • Continuously improve the data platform architecture, tooling, and development practices.

• Own design and maintenance of all aspects of data solutions including modeling, developing, technical documentation, data diagrams and data dictionaries. • Provide expertise in the development of standards, architectural governance, design patterns, and practices, evaluate best applicable solutions for different use cases • Determines and develops architectural approaches and solutions, conducts business reviews, documents current systems, and develops recommendations • Lead the data strategy and own the vision and roadmap of data products • Work with stakeholders to ensure that data related business requirements for protecting sensitive data are clearly defined, communicated, and well understood and considered as part of operational prioritization and planning • Develop, maintain, and optimize data infrastructure using Delta Lake, MLflow, and Databricks SQL to enhance data management, processing, and analytics. • Utilize Snowflake’s features such as data sharing, zero-copy cloning, and automatic scaling to optimize data storage, accessibility, and performance. • Ensure effective management of both semi-structured and structured data within Snowflake’s architecture. • Implement and manage data storage solutions using Amazon S3, perform data warehousing with Amazon Redshift. • Design and implement data integration workflows using AWS Glue to orchestrate and automate data movement and transformation. • Design and implement scalable data pipelines using tools like Apache Kafka or Apache Airflow to facilitate real-time data processing and batch data workflows. • Apply advanced analytics techniques, including predictive modeling and data mining, to uncover insights and drive data-driven decision-making.