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