About the role
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