Data Machine Learning Engineer

Rand Technology LLC•Irvine, CA

just now

About The Position

Role Summary Summary and overall objectives of the job. We are seeking a Data / ML Engineer to lead the design, optimization, and expansion of our SQL Server–based data warehouse while also developing and deploying machine learning models to support business insights, forecasting, and automation. This role combines strong data engineering fundamentals (SQL Server, data warehousing, ETL/ELT) with practical machine learning experience and a passion for adopting modern data and AI platforms. Essential Functions Data Engineering & Warehousing Design, build, and maintain a scalable SQL Server–based data warehouse. Develop and optimize complex T-SQL queries, stored procedures, and indexing strategies for performance and reliability. Build and manage ETL/ELT pipelines integrating data from SQL Server, Oracle, Microsoft Dataverse, and other enterprise systems. Implement data models and schemas to support reporting, analytics, and executive dashboards. Ensure data quality, governance, security, and documentation standards are consistently applied. Machine Learning & Advanced Analytics Develop, train, and deploy machine learning models (2–3+ years hands-on experience required). Apply ML techniques to forecasting, pricing analysis, demand patterns, and market intelligence. Build data pipelines to support model training, evaluation, and production deployment. Collaborate with business stakeholders to translate real-world problems into ML solutions. Platform & Integration Leverage Azure Data Lake, Azure Data Factory, and related Microsoft data services. Integrate data from Microsoft Dynamics 365 (CRM/ERP) and other enterprise platforms. Partner with analysts, developers, and IT teams to support Power BI and advanced analytics use cases. Innovation & Growth Stay current with modern data engineering and ML standards, tools, and best practices. Actively seek opportunities to improve data architecture, modeling approaches, and automation. Bring a “builder mindset” – hungry to learn, experiment, and implement new technologies.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related field.
4+ years of data engineering experience.
2–3+ years of hands-on machine learning modeling experience.
Strong expertise in Microsoft SQL Server (T-SQL, query optimization, indexing, performance tuning).
Proven experience building and managing data warehouses.
Experience with ETL/ELT tools (SSIS, Azure Data Factory, or similar).
Proficiency in Python for data processing, automation, and ML workflows.

Nice To Haves

Experience with Azure Data Lake, Azure Synapse, or Microsoft Fabric.
Exposure to Microsoft Dynamics 365 (CRM/ERP) and Dataverse.
Background in semiconductor, distribution, or supply chain data environments.
Experience deploying ML models into production environments.
Familiarity with CI/CD pipelines for data and ML workloads.
Strong SQL and data warehousing foundation.
Practical ML experience (not just academic).
Curious, self-driven, and eager to adopt modern data/AI platforms.
Comfortable working in a fast-moving, high-impact environment.
Business-oriented: understands how data and ML drive real outcomes.

Responsibilities

Design, build, and maintain a scalable SQL Server–based data warehouse.
Develop and optimize complex T-SQL queries, stored procedures, and indexing strategies for performance and reliability.
Build and manage ETL/ELT pipelines integrating data from SQL Server, Oracle, Microsoft Dataverse, and other enterprise systems.
Implement data models and schemas to support reporting, analytics, and executive dashboards.
Ensure data quality, governance, security, and documentation standards are consistently applied.
Develop, train, and deploy machine learning models (2–3+ years hands-on experience required).
Apply ML techniques to forecasting, pricing analysis, demand patterns, and market intelligence.
Build data pipelines to support model training, evaluation, and production deployment.
Collaborate with business stakeholders to translate real-world problems into ML solutions.
Leverage Azure Data Lake, Azure Data Factory, and related Microsoft data services.
Integrate data from Microsoft Dynamics 365 (CRM/ERP) and other enterprise platforms.
Partner with analysts, developers, and IT teams to support Power BI and advanced analytics use cases.
Stay current with modern data engineering and ML standards, tools, and best practices.
Actively seek opportunities to improve data architecture, modeling approaches, and automation.