About this role
Position Summary
Scicom Infrastructure Services 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.
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.