Manager Software & Data Engineering

Dolese Bros. Co.•Oklahoma City, OK

1d•Hybrid

About The Position

The Dolese Promise is built on a foundation of integrity driven by our passion for quality, safety, and reliability. We are one of Oklahoma's most respected employee-owned companies because of our people and our values. We strongly believe in positively impacting our communities through our products, our actions, and our financial support. Being a part of the Dolese team affords a unique opportunity to join an organization that rewards its owners through profit sharing. Our employees are one of our most important resources, which is why we promise to deliver. Dolese Delivers: Stable Foundation Treat with Respect Safe Environments Employee Focus JOB SUMMARY Grow your career with a company built on Safety, Integrity, Teamwork, and Stewardship. As a Software & Data Engineering Manager at Dolese, you will serve as a hands-on technical leader responsible for managing and developing a high-performing team of software engineers, data engineers, and analysts. This role supports software development and data engineering efforts, including overseeing the delivery of software solutions and data pipelines, as well as enabling analytics and business intelligence capabilities across the organization. The Software & Data Engineering Manager sets team direction, applies established engineering standards, and ensures the delivery of high-quality software products and data solutions in alignment with organizational goals. This position supports effective execution across the software development lifecycle (SDLC) and data platforms, partnering closely with the Data Architect, business stakeholders, and IT leadership to promote technical excellence, operational efficiency, and continuous improvement.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related field required
Minimum of eight (8) years of progressive experience in software development or data engineering.
Minimum of two (2) years of experience in a team lead, senior, or people management capacity.
Strong software engineering fundamentals including object-oriented design, RESTful API development, version control (Git), and CI/CD practices.
Proficiency in one or more programming languages such as Python, C#, Java, or TypeScript for application development or automation.
Advanced proficiency in SQL and experience working with large, complex datasets across structured and semi-structured sources.
Hands-on experience with data pipeline development, ETL/ELT frameworks, and modern cloud data platforms (e.g., Azure Synapse, Databricks, Snowflake, Microsoft Fabric).
Experience with Power BI, including data modeling (star schema, DAX), report development, and workspace governance.
Familiarity with DevOps practices including automated testing, infrastructure as code (e.g., Terraform, Bicep), and deployment pipeline tools (e.g., Azure DevOps, GitHub Actions).
Working knowledge of cloud platforms (Azure preferred) including compute, storage, networking, and security fundamentals.
Familiarity with AI/ML concepts and experience applying or overseeing predictive modeling or machine learning in a business context.
Excellent communication and stakeholder management skills with the ability to present complex technical topics clearly to executive audiences.
Demonstrated ability to set high technical standards and foster a culture of craftsmanship, accountability, and continuous learning.
Strong analytical capability with a focus on data accuracy, logical analysis, and evidence-based decision-making.
Ability to translate complex technical and data concepts into clear, actionable information for both technical and non-technical audiences.
Understanding of organizational goals with the ability to align engineering and analytics efforts accordingly.
Collaborative approach with the ability to build trust and alignment across business and technology teams.
Demonstrated ability to lead, coach, and develop engineers in a fast-paced, matrixed environment.
Demonstrated commitment to developing team members’ skills, confidence, and career growth through coaching and feedback.
Ability to manage competing priorities, adapt to change, and remain effective in a dynamic work environment.
Ongoing technical curiosity with the ability to stay current on software, AI, BI, and data platform advances and apply relevant innovations within the team.

Nice To Haves

Master’s degree in Computer Science, Software Engineering, Data Science, or a related field preferred.
Microsoft certifications such as Azure Developer Associate (AZ-204), Azure Data Engineer Associate (DP-203), Power BI Data Analyst Associate (PL-300), or Azure Solutions Architect Expert (AZ-305) preferred.
Experience with containerization and orchestration technologies (Docker, Kubernetes, Azure Container Apps) preferred.
Hands-on experience with AI-powered development tools including GitHub Copilot, Azure OpenAI, and Copilot for Power BI preferred.
Experience with Python or R for advanced analytics, automation, or ML workflow development preferred.
Familiarity with data governance and cataloging tools (e.g., Microsoft Purview, Alation, Collibra) preferred.
Experience working within agile or Scrum-based delivery frameworks preferred.
Experience in industries such as energy, manufacturing, healthcare, or financial services preferred.

Responsibilities

Lead, manage, and develop a cross-functional team of software engineers and data engineers/analysts, fostering a culture of ownership, collaboration, and continuous improvement.
Set clear performance expectations, provide regular coaching and feedback, and conduct formal performance reviews aligned with organizational standards.
Identify skill gaps and build development plans that advance proficiency across software engineering, data engineering, cloud platforms, and BI tools.
Manage team capacity and workload, balancing software product development, data engineering initiatives, and operational support demands.
Recruit, onboard, and retain top engineering talent in alignment with the organization's technology roadmap and growth objectives.
Serve as an escalation point for complex technical challenges, architectural decisions, stakeholder conflicts, and delivery blockers.
Oversee the design, development, testing, and deployment of internal software applications, APIs, integrations, and automation tools.
Enforce software engineering best practices including code reviews, version control standards (Git), CI/CD pipelines, unit testing, and secure coding principles.
Provide input and technical guidance on architectural decisions related to application design, system integrations, microservices, and cloud-native development patterns.
Ensure all software deliverables meet quality, performance, security, and maintainability standards before release.
Manage the software development backlog in collaboration with business and IT stakeholders, prioritizing features, bug fixes, and technical debt resolution.
Drive adoption of DevOps practices, including automated testing, infrastructure as code (IaC), and deployment pipeline management.
Oversee the design and delivery of data pipelines, ETL/ELT workflows, and data integration solutions supporting business applications and reporting needs.
Ensure the reliability, performance, and maintainability of data ingestion, transformation, and delivery processes supporting analytics and reporting.
Partner with the Data Architect to implement and support enterprise data platform capabilities on cloud technologies such as Azure Synapse, Microsoft Fabric, Databricks, or Snowflake.
Maintain governance over data models, schema design, and pipeline documentation in alignment with enterprise data standards.
Remain hands-on as needed to support the team on complex pipeline builds, data model design, and platform configuration challenges.
Oversee the delivery of dashboards, reports, and analytical products, ensuring accuracy, timeliness, and alignment with business requirements.
Champion the adoption of self-service BI capabilities, working with the team to maintain certified Power BI datasets, datamarts, and report templates.
Define and enforce team standards for Power BI development including naming conventions, data model design, RLS implementation, and workspace organization.
Drive business user enablement through training, documentation, and support that increases self-sufficiency in data and reporting.
Lead the team's adoption of AI/ML tools to enhance both software engineering workflows and analytical capabilities, including GitHub Copilot, Copilot for Power BI, Azure OpenAI, and intelligent automation.
Guide engineers and analysts in applying predictive modeling, machine learning, and AI-powered features to solve business problems at scale.
Support the Data Architect by identifying opportunities to apply AI-enabled capabilities within data products, such as intelligent alerting, anomaly detection, and automated narrative generation.
Stay current on emerging software, AI/ML, and data platform capabilities and evaluate tools that improve team productivity and solution quality.
Serve as the primary technology point of contact for assigned business units, building trusted relationships with department leaders, operations, finance, and other key stakeholders.
Translate strategic business needs into software and data engineering initiatives, ensuring team efforts align with organizational priorities.
Facilitate regular reviews with stakeholders to communicate roadmap progress, surface insights, and gather feedback on team performance and solution quality.
Represent the engineering team in cross-functional technology discussions, architecture reviews, and data governance councils.
Define, document, and continuously improve team workflows including intake processes, development standards, testing protocols, code review practices, and release management.
Drive automation of manual and repetitive software, data preparation, and reporting tasks to free the team for higher-value engineering work.
Establish, monitor, and communicate team KPIs, such as deployment frequency, pipeline reliability, report adoption, stakeholder satisfaction, and delivery timeliness to measure and communicate team impact.
Contribute feedback and improvements to engineering standards, architecture frameworks, and reusable solution templates in partnership with IT and data leadership.