Databricks Data Platform Engineer

• You have a bachelor's or master's degree and corresponding thinking level.

• You have 5+ years of experience in data platform engineering or data engineering in large-scale enterprise environments.

• You have hands-on experience with Databricks (data engineering, analytics, or platform administration).

• You are proficient in Python and SQL, and familiar with CI/CD practices.

• You have a solid understanding of:Data lake/lakehousearchitectures, data ingestion and transformation patterns,Cloud-native data platforms

• You are comfortable working in an agile (Scrum) environment.

• You communicate clearly and enjoy collaborating with both technical and non-technical stakeholders.

• You have a strong enablement mindset and enjoy helping others succeed.

• Experience with Azure DevOps, Terraform, Bash, Azure CLI, or REST APIs.

• Experience with data governance, security, and access control concepts (e.g.RBAC).

• Familiarity with analytics engineering tools and practices.

• Databricks certifications (Data Engineer, Platform Administrator, or Solution Architect).

• Experience working in aCenterof Excellence or similar enablement-focused setup.

DatabricksDataPlatformEngineer

For theData & AIdepartmentat NN GroupITis currently seekingan experienced Databricks Data Platform Engineer to help design,operate, and continuously improve our enterprise-grade data platforms. In this role, you will work at the intersection of platform engineering, enablement, and internal consultancy, supporting domain teams to successfully build data and AI solutions on Databricks..

You will play a key role in standardizing best practices, enabling self-service, and upskilling data engineers, analysts, and data scientists across the organization. The ideal candidate combines strong technical depth with a coaching mindset and enjoys working closely with teams to accelerate their data maturity.

• Design, build, andoperatescalable and secure Databricks data platforms in a cloud environment (Azure-based).

• Develop andmaintainshared platform components, including workspace standards, cluster policies, networking, and integrations.

• Automate platform provisioning and configuration using Infrastructure as Code and CI/CD pipelines.

• Ensure platform reliability, performance, cost-efficiency, and observability.

• Enablement & self-service

• Enable self-service data engineering and analytics capabilities by providing reusable templates, libraries, and reference architectures.

• Define and promote platform standards, guardrails, and best practices for data ingestion, transformation, and analytics on Databricks.

• Support teams in adopting these standards while balancing autonomy and governance.

• Actively remove friction for data teams by improving developer experience on the platform

• Act as a trusted technical advisor for data engineers, analytics engineers, data scientists, and product teams.

• Support domain teams in designing and implementing data pipelines and analytical use cases on Databricks.

• Partner with architects, security, and governance teams to ensure platform solutions align with enterprise policies.

• Translate business and analytical requirements into robust, scalable technical solutions.

• Contribute to and deliver training sessions, workshops, and onboarding materials for Databricks users.

• Coach and mentor engineers and analysts to raise overall data platform maturity.

• Share knowledge through documentation, internal communities, anddataengineering guilds.

• Actively contribute to a strong Data & AI engineering culture within the organization.