Databricks Practice Lead / Engineering Manager

Job summary

Position Summary Sci*** is seeking an experienced Databricks Practice Lead / Engineering Manager to provide hands-on technical leadership while managing a team of data engineers, architects, and consultants supporting complex enterprise and government programs.

Qualifications

• into technical plans and deliverables.
• Develop project estimates, staffing plans, delivery schedules, milestones, and risk-mitigation strategies.
• Monitor project scope, schedule, quality, budget, resource utilization, dependencies, and technical risks.
• Ensure deliverables meet client
• internal quality standards, security controls, and contractual commitments.
• Coordinate work across engineering, cloud, cybersecurity, data governance, analytics, project-management, and client teams.
• Track delivery metrics and provide clear status reports to internal leadership, clients, and program stakeholders.
• Lead technical escalations and ensure issues are resolved promptly and appropriately documented.
• Support statements of work, technical proposals, solution estimates, presentations, and client demonstrations.
• Participate in client meetings as the technical and delivery authority for Databricks-related work.
• Required
• Bachelor’s degree in computer science, information technology, data engineering, engineering, or a related discipline.

Responsibilities

• Databricks Technical Leadership
• Serve as the organization’s subject-matter expert for the Databricks Lakehouse Platform.
• Design scalable, secure, and highly available data architectures using Databricks, Apache Spark, Delta Lake, and cloud-native
• based on project needs, employee strengths, availability, and technical complexity.
• Establish measurable goals, performance expectations, development plans, and technical competency standards.
• Conduct regular one-on-one meetings, performance reviews, coaching sessions, and technical development activities.
• Support recruiting, interviewing, candidate evaluation, onboarding, and workforce planning.
• Identify technical or performance gaps and coordinate training, mentoring, or corrective action as appropriate.
• Promote collaboration, accountability, documentation, knowledge sharing, and continuous improvement.
• Develop reusable accelerators, reference architectures, templates, and delivery playbooks.
• Build and maintain a strong Databricks practice capable of supporting multiple concurrent client engagements.
• Program and Delivery Management

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