[Hiring] Databricks Practice Lead / Engineering Manager @Scicom Infrastructure Services

Role Description

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.

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

• 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

• 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.