Data Engineering Manager

• 10+ years of experience in data engineering, data architecture, or related disciplines, including 5+ years leading senior engineers or technical teams and mentoring high-performing engineering organizations. \n

• Proven experience architecting, building, and scaling enterprise data platforms, including data lakes, data warehouses, pipelines, streaming architectures, and large-scale data integration environments. \n

• Strong cloud data engineering experience (preferably Azure) with hands-on architectural knowledge of platforms/technologies such as Azure SQL, Microsoft Fabric, Databricks, Snowflake, Kafka, or comparable tools. \n

• Experience establishing enterprise data governance and data-quality frameworks, engineering standards, platform reliability practices, scalability approaches, and cost optimization across complex data environments. \n

• Demonstrated ability to set data engineering strategy, influence technical and business stakeholders, and partner effectively across Analytics, AI/ML, Product, and executive leadership. \n, * Experience leading data platform, system, or data-integration efforts tied to mergers and acquisitions, including harmonizing data models and consolidating pipelines and tooling. \n

• Background in IT Services, Managed Services, or similar enterprise technology environments, particularly supporting ERP, ServiceNow, or supply-chain data ecosystems. \n

• Relevant cloud or data-platform certifications (Azure, AWS, GCP, or equivalent advanced credentials). \n

Compensation: up to $170k salary/year + Profit Sharing = up to $190k total compensation/year

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Benefits: 100% company paid medical (for you + your family), dental, vision, LTD/STD, HSA/FSA, term life, supplemental health insurances (e.g., Aflac), 12-weeks paid maternity leave, 401(k) + 3% company match, Unlimited PTO

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This senior leadership role owns the architecture, reliability, and evolution of an enterprise data platform that powers analytics and AI/ML use cases. Success is measured by building a scalable, cost-effective cloud data ecosystem, raising engineering standards and data quality across the organization, and growing a high-performing data engineering team. You will partner closely with technical and business leaders to align platform investments to measurable outcomes.

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• Own the enterprise data engineering platform strategy, including target architecture, multi-quarter roadmap, and prioritized delivery plans aligned to business goals. \n

• Lead the design and scaling of data lakes, data warehouses, batch/streaming pipelines, and large-scale data integration patterns to support enterprise reporting and advanced analytics. \n

• Establish engineering standards for data modeling, pipeline development, testing, CI/CD, observability, documentation, and operational readiness across the data platform. \n

• Drive cloud architecture decisions (preferably Azure) across storage, compute, security, and networking to improve reliability, performance, and maintainability. \n

• Implement and continuously improve data governance and data-quality frameworks, including data lineage, metadata management, validation rules, SLAs, and issue triage processes. \n

• Manage platform reliability by defining SLOs, monitoring and alerting, incident response practices, and post-incident reviews that lead to concrete preventive actions. \n

• Optimize platform costs by creating chargeback/showback visibility, right-sizing workloads, reducing waste, and setting guardrails for efficient resource usage. \n

• Lead, hire, and mentor senior data engineers and technical leads; set clear expectations, coach performance, and build succession plans for critical roles. \n

• Partner with Analytics, AI/ML, Product, and executive stakeholders to translate needs into platform capabilities, tradeoffs, and delivery milestones. \n

• Govern architecture reviews and technical decision-making to ensure solutions are secure, scalable, and consistent with enterprise standards and long-term direction. \n

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