JOB SUMMARY:
The Manager - Data Engineering leads a team responsible for designing, building, and operating trusted, scalable data products that enable reporting, analytics, AI, and operational use cases. This role ensures that data pipelines, data models, and platform capabilities are delivered with strong engineering practices (automation, testing, observability, and security) and are aligned to business priorities. The manager promotes adoption of modern platforms such as Microsoft Fabric and Azure data services and fosters a collaborative environment that emphasizes technical excellence, continuous learning, and cross-functional engagement.
The Manager - Data Engineering is an IT leadership role focused on building and continuously improving a mature data platform and reusable data products that power enterprise reporting and self-service analytics, enable AI/ML solutions, and support operational decision-making. This manager partners with IT and business stakeholders to define data product roadmaps, establish engineering standards, and ensure reliable deployment and performance across environments.
JOB RESPONSIBILITIES:
• Lead the design, development, and deployment of data products (pipelines, curated datasets, and semantic layers) that enable scalable reporting and analytics.
• Partner with stakeholders to translate business needs into data product roadmaps, platform capabilities, and prioritized engineering backlogs.
• Establish and enforce engineering best practices for data ingestion, transformation, testing, version control, CI/CD, and release management.
• Manage and mentor data engineers; build team capability across data modeling, cloud services, automation, and operational support.
• Ensure data quality, documentation, and governance are embedded in delivery (metadata, lineage, access controls, and stewardship coordination).
• Operate and optimize the data platform to meet reliability, performance, and cost targets (monitoring, incident response, capacity planning, and cost management).
EDUCATION/ EXPERIENCE:
• Bachelor's degree required in MIS, Computer Science, Data Analytics, Engineering, Mathematics, or related field.
• Master's degree preferred.
• Minimum 7 years of relevant experience
• Experience with SQL and Python, plus modern data engineering platforms/services (e.g., Microsoft Fabric, Azure Data Factory, Synapse, Databricks, or equivalent) required.
• Experience leading data engineering initiatives from concept to production, including stakeholder engagement, delivery planning, and mentoring engineers/analysts required.
QUALIFICATONS:
• Strong written and verbal communication skills
• Experience with Microsoft Office including Excel and PowerPoint
• Practical application experience with data engineering tools and languages such as SQL, Python, Spark, and orchestration frameworks (e.g., Azure Data Factory or equivalent)
• Practical application experience with data architecture patterns and storage technologies (data lakes/lakehouse, warehouses, relational databases) and developing curated, reusable datasets
• Preferred experience building and operating ETL/ELT pipelines (batch and/or streaming), including data modeling, automated testing, monitoring, and incident triage
• Ability to communicate data architecture and data product concepts to a broad audience and partner effectively with reporting/analytics teams on downstream enablement
• Desire and ability to learn and leverage new software, tools, and processes in a self-learning environment
• Preferred experience managing and optimizing cloud/IT tooling costs (e.g., cost awareness, chargeback/showback concepts, and right-sizing) while meeting performance and reliability needs
• Preferred familiarity with AI/ML enablement patterns (e.g., feature-ready datasets, governed access, and integration with tools such as Azure ML). While model development is not a core responsibility, awareness of downstream AI needs is beneficial.
• Proven experience leading data engineering and data product delivery from concept to production, including stakeholder engagement, resource coordination, deployment planning, and operational ownership. Ability to mentor engineers/analysts and drive strategic initiatives across teams.
The above statements are intended to describe the general nature and level of work being performed by employees assigned to this job. They are not intended to be an exhaustive list of all responsibilities, duties, and skills required of personnel so classified.
