Role Description We are seeking an experienced Senior Data Engineer to join our Data & BI team within the manufacturing and distribution sector. This role will focus on implementing and maintaining an Azure cloud-based data platform and building scalable medallion architecture solutions to support enterprise data initiatives. This is a delivery-focused, hands-on role with primary responsibilities in implementation, platform maintenance, operational excellence, and supporting modernization initiatives. This will be a remote position, and it can be based anywhere in the Continental US. Key Responsibilities - Implementation & Delivery - Implement cloud-based, scalable data platforms to support manufacturing operations, inventory management, and distribution analytics. - Build and maintain Lakehouse and develop ELT/ETL workflows using Databricks, Lakeflow, Azure Data Factory, FiveTran, and Azure Data Lake Storage. - Integrate data from ERP and different source systems, including JD Edwards (JDE), Hubspot, Oracle, SQL Server, etc. - Configure and maintain Unity Catalog — applying access policies, lineage settings, and metadata as directed by the architectural governance framework. - Implement RBAC/ABAC across the data platform, including row-level and column-level security in UC and ADLS. - Monitor pipeline health, troubleshoot failures, and perform root-cause analysis on data quality or performance issues. - Participate in daily support and respond to production incidents promptly. - Knowledge Sharing & Documentation - Actively share knowledge and produce clear technical documentation for all implemented solutions — including pipeline designs, data flow diagrams, runbooks, and configuration notes. - Maintain up-to-date documentation in the team's knowledge base. - Contribute to team standards and best practices. Qualifications - 5+ years of hands-on data engineering experience. - Strong Python skills. - Proficient with Databricks — Delta Lake, PySpark/Spark SQL, notebooks, jobs, and cluster management. - Proficient with Azure, Azure Data Lake Storage Gen2 and the broader Azure data ecosystem, Azure Data Factory, Azure Logic Apps, and Azure Key Vault. - Practical experience with connector management, API and Databricks Lakeflow (Workflows / DLT pipelines). - Hands-on experience with medallion architecture, dimensional modeling, and incremental load. - 5+ years working with Oracle (PL/SQL, stored procedures) and SQL Server (T-SQL, CDC, query optimization); experience extracting from JD Edwards (JDE) table structures and business data models. - Hands-on experience implementing RBAC/ABAC, row-level security, and column-level security across Databricks, Unity Catalogue, and relational databases. - Experienced with Git, Azure DevOps or GitHub Actions, ansible and CI/CD pipelines. - Excellent communication skills, with the ability to translate complex data into actionable business insights. - Familiar with Agile methodology, participate in Sprint planning. Preferred Qualifications - Exposure to streaming data patterns (Event Hubs, Kafka, or Structured Streaming). - Knowledge and hands-on experience with Power BI. - Familiarity with Azure Functions, Azure ML, Logic Apps, Azure Synapse, Microsoft Fabric, and Oracle Cloud. - Knowledge of FiveTran, dbt, Machine Learning, and AI development and deployment. Compensation RectorSeal has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market position. Employees may be eligible for pay incentives based on overall corporate and individual performance or at the discretion of the CSWI Board of Directors. Benefits - Comprehensive medical, dental, and vision plans. - 401(k) and pension. - Life insurance. - Paid time off program with paid holidays. - Various wellness programs.

• Manage, mentor, and grow a 10-person data engineering team and a 3-person AI/ML team; own headcount planning and hiring across both • Set a unified roadmap where data infrastructure and AI/ML development reinforce each other • Build a culture of technical rigor, ownership, and delivery • Lead development of ML models using proprietary insurance data: risk scoring, pricing signals, anomaly detection, loss prediction • Own LLM integration strategy from prompt engineering and RAG pipelines to fine-tuning and agentic workflows • Drive AI automation across operations: underwriting intake, document processing, triage, internal tooling • Partner with the CTO on enterprise AI platform decisions: tooling, deployment infrastructure, model governance • Build the evaluation, monitoring, and feedback loops that turn experiments into production systems • Set architectural standards for pipelines, data modeling, and platform infrastructure • Own reliability, observability, and data quality across Snowflake, dbt, Airflow, and Terraform • Build semantic layers and data models that serve underwriting, pricing, finance, and executive reporting • Establish data governance, quality frameworks, and documentation standards that scale • Collaborate with actuaries, underwriters, engineers, and product leaders to translate business needs into AI and data solutions • Operate as a senior technical voice in planning, roadmap, and strategy discussions

• Design, develop, and maintain scalable data pipelines and data platforms • Build and optimize ETL/ELT processes for large-scale data processing • Work with Databricks to develop and manage data workflows and analytics solutions • Implement data processing solutions using Apache Spark and modern data engineering practices • Collaborate with data scientists, analysts, and business stakeholders to enable data-driven insights • Ensure data quality, reliability, and performance of data pipelines • Contribute to architecture decisions and data platform improvements • Mentor junior engineers and promote best practices in data engineering

• Design, develop, and optimize database schemas, stored procedures, functions, triggers, and views in SQL Server and other RDBMS platforms. • Provide production database support, including incident response, troubleshooting, root-cause analysis, and resolution of performance and availability issues. • Plan and execute data patching, hotfixes, and schema changes across development, test, and production environments with minimal downtime. • Perform performance tuning, query optimization, indexing strategy, and execution-plan analysis. • Administer database instances: installation, configuration, upgrades, patching, backup and recovery, and disaster recovery planning. • Manage and support databases in cloud environments — Azure SQL Database, Azure SQL Managed Instance, SQL Server on Azure VMs, or equivalent services on other cloud platforms. • Implement and maintain security controls, access management, encryption, and compliance requirements. • Monitor database health, capacity, and resource utilization; set up alerting and proactive maintenance. • Support migrations between on-premises and cloud, and between database versions. • Collaborate with development teams on data modeling, release management, and deployment automation. • Maintain documentation for configurations, procedures, and runbooks.