Databricks Practice Lead / Engineering Manager

Position Summary Scicom Infrastructure Services is seeking an experiencedDatabricks Practice Lead / Engineering Managerto provide hands-on technical leadership while managing a team of data engineers, architects, and consultants supporting complex enterprise and government programs. This role requires a senior Databricks expert who can design and oversee modern data platforms, establish technical standards, guide delivery teams, and remain actively involved in architecture, troubleshooting, code reviews, and client-facing solution development. The successful candidate will balance deep technical expertise with strong people leadership, delivery management, and stakeholder communication skills. Key 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 technologies. - Lead the implementation of batch, streaming, ETL, ELT, analytics, machine-learning, and AI-enabled data solutions. - Define architectural standards for medallion architectures, data modeling, ingestion, transformation, orchestration, and data consumption. - Establish governance frameworks using Unity Catalog, including data lineage, access controls, auditing, metadata management, and secure data sharing. - Guide Databricks workspace design, cluster configuration, serverless computing, workload isolation, performance tuning, and cost optimization. - Oversee integration between Databricks and cloud platforms such as Microsoft Azure, AWS, or Google Cloud. - Develop or review solutions involving PySpark, Spark SQL, Python, Delta Live Tables, Structured Streaming, Auto Loader, MLflow, and Databricks Workflows. - Lead platform migrations and modernization efforts from legacy databases, data warehouses, Hadoop environments, and traditional ETL platforms. - Establish development standards for source control, automated testing, CI/CD, infrastructure as code, monitoring, and production support. - Conduct architecture reviews, code reviews, technical assessments, and root-cause analyses. - Evaluate emerging Databricks capabilities and recommend appropriate adoption strategies. Team Leadership and Management - Manage, mentor, and develop a team of Databricks engineers, data engineers, architects, and technical consultants. - Assign resources and responsibilities 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 - Provide delivery oversight for Databricks and data-engineering projects from planning through implementation and operational support. - Translate business, functional, security, and contractual requirements 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 requirements, internal quality standards, security controls, and contractual commitments. - Coordinate work across engineering, cloud, cybe