Solutions Architect

About the role

This senior architecture role owns the design and modernization of enterprise data platforms on Azure, with a primary focus on Databricks Lakehouse patterns, Delta Lake, and cloud-native data services. The role turns complex business requirements into scalable technical designs that support reporting, advanced analytics, AI/ML, and business-critical decisioning.

Responsibilities

• Own end-to-end architecture and solution design for enterprise Azure data platforms, ensuring the Databricks Lakehouse is built for scalability, reliability, and measurable business impact.
• Define target-state architecture, ingestion patterns, transformation frameworks, and serving layers that support reporting, advanced analytics, machine learning, and critical decisioning use cases.
• Design and implement ETL and ELT pipelines using PySpark, SQL, Databricks Workflows, Auto Loader, and Delta Live Tables for batch and near real-time processing.
• Establish architecture standards for data modeling, medallion design, reusable engineering patterns, CI/CD, code quality, environment strategy, and release management across Databricks solutions.
• Drive governance and security through Unity Catalog, RBAC and ABAC controls, lineage, auditability, and integration with enterprise governance services such as Purview.
• Optimize platform performance by tuning Spark workloads, cluster policies, partitioning, file sizing, caching, and compute cost management for large-scale processing.
• Partner with stakeholders, architects, analysts, and downstream consumers to convert functional and non-functional requirements into scalable technical solutions.
• Provide technical leadership to engineering teams through design reviews, implementation guidance, bottleneck resolution, and best-practice setting for Databricks delivery.

Requirements

Essential Skills & Technologies:

• Strong architecture experience across Lakehouse, medallion, data modeling, data warehousing, and scalable ingestion and transformation frameworks, with clear ability to design for enterprise outcomes.
• Deep hands-on expertise in Databricks, Delta Lake, PySpark, Python, SQL, Databricks Workflows, Auto Loader, and Delta Live Tables for high-performance data engineering delivery.
• Strong Azure platform knowledge, including Azure Data Factory, Azure Data Lake Storage, Azure Key Vault, Azure DevOps, and integration with broader cloud ecosystems.
• Practical understanding of governance, lineage, and security controls, including Unity Catalog, access management, and integration with enterprise governance tools such as Azure Purview.
• Proven ability to tune large Spark and Databricks workloads, including workload management, cluster sizing, partitioning strategy, and cost optimization at scale.
• Strong communication and leadership skills to mentor teams, review technical designs, and influence architecture decisions across cross-functional programs.

Skills

• Knowledge of insurance data models, regulatory considerations, and analytics use cases tied to underwriting, claims, pricing, or risk functions.
• Experience recommending advanced Databricks capabilities such as Photon, serverless compute, Lakehouse Federation, and streaming patterns.
• Background in Agile delivery governance, including sprint planning, backlog refinement, dependency management, and technical planning.

Plus

Additional Plus Knowledge:

• Knowledge of insurance data models, regulatory considerations, and analytics use cases tied to underwriting, claims, pricing, or risk functions.
• Experience recommending advanced Databricks capabilities such as Photon, serverless compute, Lakehouse Federation, and streaming patterns.
• Background in Agile delivery governance, including sprint planning, backlog refinement, dependency management, and technical planning.

Qualifications

Qualifications:

• 10–15 years in data engineering, cloud data platform design, or enterprise data architecture, with at least 5 years of strong hands-on Databricks and Azure experience.
• A proven record of turning business requirements into scalable solution designs and implementation roadmaps that improve delivery quality and platform value.
• Strong ownership of architecture standards, delivery best practices, and technical decision-making across enterprise-scale data platforms.
• The ability to balance performance, security, governance, and cost efficiency while guiding engineering teams toward durable outcomes.

Pay

TBD

Schedule

TBD