Senior Data Architect

Job Description

Overview

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Gravitas is seeking a Senior Data Architect to lead the design and delivery of modern data platforms and analytics architectures within a fast-paced, client-facing environment. You will act as the Databricks champion within the business, shaping best practice, guiding delivery teams, and supporting stakeholders through pre-sales and solution definition.

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Key Responsibilities

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• Own end-to-end data architecture across lakehouse, warehouse, and streaming patterns, ensuring scalability, security, and governance.

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• Act as Databricks champion: define standards, reference architectures, accelerators, and reusable assets.

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• Lead architectural design for Databricks (workspaces, clusters, jobs, Delta Lake, Unity Catalogue, MLflow) and integrations with cloud services.

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• Partner with pre-sales teams to shape propositions, run discovery, produce high-quality proposals, and present solutions to senior client audiences (pre-sales experience is essential).

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• Translate business requirements into target-state data models, ingestion patterns, and semantic layers.

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• Guide engineers on implementation, performance optimisation, cost control, CI/CD, and operational readiness.

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• Ensure compliance with data governance, privacy, and security controls (RBAC, encryption, auditing, lineage).

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Essential Requirements

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• Significant Databricks experience required, including production-scale deployments and optimisation.

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• Strong hands-on capability with Spark/SQL, Delta Lake, orchestration, and data engineering patterns.

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• Prior experience within a consulting practice with exposure to both pre-sales and delivery leading on RFI and RPF responses, discovery, risk and high-level estimates.

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• Extensive experience designing cloud data platforms (AWS, Azure, or GCP) and integrating with enterprise systems.

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• Proven stakeholder management and ability to communicate architecture to both technical and non-technical audiences.

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• Experience with data modelling (dimensional, Data Vault, and/or domain-oriented approaches) and metadata management.

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• Working knowledge of DevOps practices, infrastructure as code, and automated testing for data pipelines.

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Desirable

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• Databricks Certifications highly desired (e.g., Data Engineer, Data Analyst, Machine Learning, or Architect).

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• Experience delivering governance frameworks, catalogue implementations, and operating models.

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• Exposure to MLOps and analytics enablement.

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