Lead Client-Facing Architecture Discussions: Work directly with client stakeholders to understand business objectives, technical requirements, existing data environments, and architectural challenges. Translate these needs into practical, scalable data architecture solutions.

Define Enterprise Data Architecture: Design and recommend modern data platform architectures using Azure Databricks, Delta Lake, and cloud-based data engineering technologies, ensuring alignment with client objectives and enterprise standards.

Drive Technical Discovery and Solution Design: Facilitate technical workshops, discovery sessions, and architecture reviews to assess current-state environments, identify gaps, evaluate solution options, and define target-state architectures.

Provide Hands-On Technical Leadership: Develop, validate, and review data engineering solutions using SQL, Python/PySpark, Apache Airflow, and dbt Core, balancing architectural oversight with practical implementation expertise.

Guide Data Pipeline Development: Establish architecture and implementation standards for ingestion pipelines that integrate APIs, databases, files, and cloud data sources using Databricks-native capabilities such as Lakeflow Connect and Auto Loader.

Establish Data Architecture Standards: Define and promote reusable architectural patterns, implementation guidelines, and engineering best practices across client projects and distributed delivery teams.

Implement Medallion Architecture: Design and guide the implementation of Bronze, Silver, and Gold data layers to support reliable ingestion, standardized transformations, curated data products, and downstream analytics.

Drive Data Governance and Modeling: Establish appropriate practices for Unity Catalog, data access controls, lineage, semantic modeling, data discoverability, and enterprise data governance.

Oversee Technical Delivery and Quality: Review partner and engineering team deliverables, including architecture designs, code, data models, and pipelines, ensuring alignment with requirements, performance expectations, security standards, and long-term maintainability.

Champion Data Quality and Reliability: Define standards for data validation, automated testing, pipeline monitoring, error handling, and operational readiness to ensure reliable production data products.

Support CI/CD and Engineering Automation: Guide implementation of Git-based workflows, automated testing, CI/CD pipelines, and controlled deployment practices across development, testing, and production environments.

Advise on Performance and Cost Optimization: Evaluate platform performance, processing efficiency, scalability, and cloud consumption, recommending improvements that balance technical requirements with business value.

Drive AI-Assisted and Spec-Driven Development: Encourage the effective use of AI-assisted development tools and specification-driven engineering practices while maintaining architectural integrity, code quality, and delivery standards.

Coordinate Distributed Engineering Teams: Provide technical direction, resolve architectural questions, and facilitate collaboration between client stakeholders, internal engineers, and external technology partners.

Communicate Progress and Technical Risks: Clearly communicate architectural decisions, implementation trade-offs, dependencies, risks, and recommendations to both technical and non-technical stakeholders.

Support End-to-End Delivery: Maintain architectural ownership throughout the project lifecycle, from discovery and solution design through implementation, deployment, and ongoing optimization.

8+ years of overall experience in data engineering, data architecture, or related technical roles, with strong hands-on experience designing and delivering production-grade data platforms.

Proven experience in a client-facing technical role, such as Data Architect, Solutions Architect, Technical Consultant, or Lead Data Engineer, working directly with enterprise customers or stakeholders.

Strong hands-on expertise with Azure Databricks, including the design and implementation of scalable enterprise data solutions.

Deep understanding of Delta Lake, including data management, processing patterns, and performance optimization.

Experience with Unity Catalog, including data governance, access controls, lineage, and enterprise data management.

Strong proficiency in SQL and Python/PySpark for data processing, transformation, and pipeline development.

Hands-on experience with Apache Airflow for orchestration and dbt Core for data transformation, testing, and modeling.

Experience designing ingestion pipelines across APIs, relational databases, files, and other enterprise data sources.

Familiarity with Databricks-native ingestion technologies, including Lakeflow Connect and Auto Loader.

Strong understanding of Medallion Architecture, data products, semantic modeling, and data quality frameworks.

Experience with CI/CD, Git, automated testing, monitoring, and production data pipeline management.

Demonstrated ability to review and challenge technical architectures, code, data models, and engineering deliverables while providing constructive, actionable feedback.

Experience translating business requirements into technical specifications, architectural designs, and implementation roadmaps.

Ability to evaluate technical trade-offs and communicate recommendations clearly to technical teams, business stakeholders, and client leadership.

Experience working with external technology partners, third-party delivery teams, or distributed engineering organizations.

Familiarity with AI-assisted development tools and spec-driven development practices.

Strong communication, presentation, stakeholder management, and problem-solving skills.

Ability to manage competing priorities, navigate ambiguity, and build trusted relationships with clients while maintaining technical credibility.