Data Architect
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Job Description
Data Architect to design and operationalize KSM’s Databricks lakehouse platform. Responsibilities include establishing architecture standards and partnering with engineering teams.
Responsibilities:
- Define and maintain KSM’s Databricks lakehouse architecture, including ingestion, storage, transformation, modeling, and access patterns
- Establish clear, repeatable design standards for Bronze, Silver, and Gold data layers to ensure consistency and reuse
- Design architecture that supports structured, semi-structured, and unstructured data across enterprise systems
- Design and implement data governance, lineage, and quality frameworks that ensure data is trusted, auditable, and scalable
- Enable discoverability, access control, and metadata management using Databricks Unity Catalog and related tooling
- Partner with data and analytics teams to define validation standards, reconciliation processes, and ownership models
- Partner closely with the Senior Data Engineer to translate architectural standards into production-ready pipelines
- Review and validate data pipelines, models, and workflows to ensure alignment with architectural best practices
- Support teams by providing guidance, patterns, and examples rather than one-off solutions
- Define best practices for Databricks compute and storage optimization, balancing performance and cost
- Establish architectural patterns that promote reliability, scalability, and operational simplicity
- Collaborate on monitoring, alerting, and operational standards to ensure platform health
- Define and support CI/CD standards for data pipelines and lakehouse infrastructure
- Partner with engineering teams to implement Infrastructure as Code (IaC) using tools such as Terraform
- Ensure consistent, automated deployments across development, test, and production environments
- Align data architecture decisions with firm-wide analytics, automation, and AI strategy
- Help prioritize architectural investments that deliver near-term value while supporting long-term growth
- Evaluate emerging data platform capabilities thoughtfully, focusing on practical adoption rather than experimentation for its own sake.
Requirements:
- 7+ years of experience in data architecture, data engineering, or enterprise data platform design
- Proven experience designing and implementing Databricks or similar modern lakehouse architectures
- Strong understanding of modern data architecture patterns, including medallion architecture and real-time or near-real-time data processing
- Deep expertise in data modeling (conceptual, logical, and physical) for analytics and BI use cases
- Hands-on experience with SQL, Python, and PySpark
- Experience implementing data governance, lineage, and quality frameworks
- Familiarity with Power BI or similar BI and analytics tools
- Strong collaboration skills with the ability to work effectively across technical and business teams
- Excellent communication and documentation skills, with the ability to clearly explain architectural decisions.
Benefits:
- flexibility to manage your time
- resources to grow
- team that genuinely cares about your success
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