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, 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 Qualifications - Bachelor’s degree in computer science, information technology, data engineering, engineering, or a related discipline. - At least 10 years of experience in data engineering, data architecture, analytics engineering, or related technology roles. - At least 5 years of hands-on experience designing and implementing solutions using Databricks. - At least 3 years of experience managing or formally leading technical engineering teams. - Advanced experience with: - Databricks Lakehouse Platform - Apache Spark and PySpark - Spark SQL and advanced SQL development - Delta Lake and medallion architecture - Unity Catalog and enterprise data governance - ETL and ELT pipeline architecture - Batch and real-time data processing - Data modeling and data warehousing - Python-based data engineering - Databricks Workflows, Jobs, and cluster management - Experience deploying Databricks solutions in Azure, AWS, or Google Cloud. - Experience with CI/CD, Git-based development, automated testing, and infrastructure as code. - Demonstrated ability to optimize Spark workloads, cluster configurations, query performance, reliability, and cloud costs. - Experience managing technical delivery, resource assignments, risks, schedules, and client expectations. - Strong written, verbal, presentation, documentation, and stakeholder-management skills. - Ability to explain complex technical concepts to executives, business stakeholders, and nontechnical audiences. Preferred Qualifications - Databricks Certified Data Engineer Professional, Databricks Certified Data Engineer Associate, or Databricks Certified Machine Learning Professional. - Databricks Certified Data Architect or comparable advanced architecture credentials. - Microsoft Azure, AWS, or Google Cloud professional-level certification. - Experience working in a consulting, professional-services, systems-integration, or managed-services environment. - Experience supporting federal, state, or local government clients. - Experience working with major consulting or systems-integration partners. - Knowledge of federal security, privacy, governance, and compliance requirements. - Experience with Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics, AWS Glue, Amazon S3, Snowflake, dbt, Kafka, Airflow, or Terraform. - Experience with MLflow, MLOps, generative AI, Databricks Mosaic AI, vector search, or machine-learning deployment. - Familiarity with data standards, metadata frameworks, data catalogs, data-sharing protocols, and open-data environments. - Experience managing geographically distributed or remote technical teams. - Experience contributing to proposals, technical responses, statements of work, and project estimates. Leadership Competencies The successful candidate will demonstrate: - Hands-on technical credibility and sound architectural judgment. - The ability to lead without becoming disconnected from the technology. - Strong accountability for team performance and project outcomes. - Effective coaching, delegation, and conflict-resolution skills. - Clear and proactive communication with clients and internal leadership. - The ability to manage competing priorities in a fast-paced consulting environment. - A commitment to quality, security, documentation, and continuous improvement. Success Measures Performance in this role will be evaluated based on: - Quality, scalability, security, and reliability of Databricks solutions. - On-time and within-budget delivery of client commitments. - Team performance, retention, development, and technical growth. - Client satisfaction and effective stakeholder communication. - Reduction in delivery risks, production incidents, and technical debt. - Adoption of standardized architectures, engineering practices, and reusable solutions. - Effective management of Databricks consumption, infrastructure, and cloud costs. - Growth and maturity of the organization’s Databricks practice. Originally posted on Himalayas