Data Engineering Manager Jobs

About The Position

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.

Benefits

• profit sharing