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- Tasks: Lead the design and implementation of a cutting-edge Databricks data platform from scratch.
- Company: Join a forward-thinking company on a mission to revolutionise data-driven initiatives.
- Benefits: Enjoy hybrid working, competitive pay, and opportunities for professional growth.
- Why this job: Be part of an exciting greenfield project that shapes the future of data architecture.
- Qualifications: Proven experience with Databricks, Azure services, and strong programming skills in Python and SQL.
- Other info: This is a 12-month contract role with potential for extension.
The predicted salary is between 72000 - 108000 £ per year.
Location: Hybrid working (London)
Duration: 12-month initial contract
Are you a visionary Databricks Architect with a passion for building cutting-edge data platforms from the ground up? Do you thrive on shaping strategy and driving technical excellence in a greenfield environment?
Our client is embarking on a pivotal journey to establish a brand-new, enterprise-grade data platform using the full power of Databricks. This is a unique opportunity to lead the architectural design and implementation of a truly greenfield data ecosystem that will underpin all future data-driven initiatives, from advanced analytics to AI/ML.
We are looking for a hands-on architect who can translate business needs into robust, scalable, and secure Databricks solutions.
The Role: As our Databricks Architect, you will be instrumental in defining and delivering our new data strategy and architecture. This is a greenfield project, meaning you'll have the exciting challenge of building the entire Databricks Lakehouse Platform from scratch. You will provide critical technical leadership, guidance, and hands-on expertise to ensure the successful establishment of a scalable, high-performance, and future-proof data environment.
Phase 1: Strategic Vision & Blueprint
- Data Strategy & Roadmap: Collaborate with business stakeholders and leadership to define the overarching data vision, strategy, and a phased roadmap for the Databricks Lakehouse Platform.
- Architectural Design: Lead the end-to-end design of the Databricks Lakehouse architecture (Medallion architecture), including data ingestion patterns, storage layers (Delta Lake), processing frameworks (Spark), and consumption mechanisms.
- Technology Selection: Evaluate and recommend optimal Databricks features and integrations (e.g., Unity Catalog, Photon, Delta Live Tables, MLflow) and complementary cloud services (e.g., Azure Data Factory, Azure Data Lake Storage, Power BI).
- Security & Governance Frameworks: Design robust data governance, security, and access control models within the Databricks ecosystem, ensuring compliance with industry standards and regulations.
Phase 2: Core Platform Build & Development
- Hands-on Implementation: Act as a lead engineer in the initial build-out of core data pipelines, ETL/ELT processes, and data models using PySpark, SQL, and Databricks notebooks.
- Data Ingestion & Integration: Establish scalable data ingestion frameworks from diverse sources (batch and streaming) into the Lakehouse.
- Performance Optimization: Design and implement solutions for optimal data processing performance, cost efficiency, and scalability within Databricks.
- CI/CD & Automation: Develop and implement Continuous Integration/Continuous Delivery (CI/CD) pipelines for automated deployment of Databricks assets and data solutions.
Phase 3: Enablement, Optimisation & Transition
- Team Enablement: Provide mentorship and technical guidance to a growing team of Data Engineers and Analysts, fostering best practices and Databricks expertise.
- Data Quality & Monitoring: Implement comprehensive data quality checks, monitoring, and alerting mechanisms to ensure data integrity and reliability.
- MLOps Integration: Lay the groundwork for seamless integration with Machine Learning Operations (MLOps) capabilities for future AI initiatives.
- Documentation & Knowledge Transfer: Create comprehensive technical documentation and conduct knowledge transfer sessions to ensure long-term sustainability of the platform.
Required Skills & Experience
- Proven Databricks Expertise: Deep, hands-on experience designing and implementing solutions on the Databricks Lakehouse Platform (Delta Lake, Unity Catalog, Spark, Databricks SQL Analytics).
- Cloud Data Architecture: Extensive experience with Azure data services (e.g., Azure Data Factory, Azure Data Lake Storage, Azure Synapse) and architecting cloud-native data platforms.
- Programming Proficiency: Expert-level skills in Python (PySpark) and SQL for data engineering and transformation. Scala is a strong plus.
- Data Modelling: Strong understanding and practical experience with data warehousing, data lake, and dimensional modelling concepts.
- ETL/ELT & Data Pipelines: Proven track record of designing, building, and optimizing complex data pipelines for both batch and real-time processing.
Desirable Skills & Certifications
- Databricks Certified Data Engineer Associate/Professional.
- Microsoft Certified: Azure Data Engineer Associate (DP-203) or Azure Solutions Architect Expert (AZ-305/304).
- Experience with other cloud providers (AWS, GCP).
- Knowledge of streaming technologies (Kafka, Event Hubs).
Data Architect employer: Osmii
Contact Detail:
Osmii Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Architect
✨Tip Number 1
Familiarise yourself with the latest features of Databricks, especially those mentioned in the job description like Delta Lake and Unity Catalog. Being able to discuss these technologies confidently during your interview will show that you're not just knowledgeable but also genuinely interested in the role.
✨Tip Number 2
Network with professionals in the data architecture field, particularly those who have experience with Databricks. Engaging in relevant online communities or attending meetups can provide insights and connections that might give you an edge in the hiring process.
✨Tip Number 3
Prepare to showcase your hands-on experience by discussing specific projects where you've implemented data solutions using Databricks. Highlighting your practical skills will demonstrate your capability to lead the architectural design and implementation as required in this role.
✨Tip Number 4
Understand the business side of data architecture by researching how data strategies impact overall business goals. Being able to articulate how your technical decisions can drive business value will set you apart from other candidates.
We think you need these skills to ace Data Architect
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your specific experience with Databricks and cloud data architecture. Focus on relevant projects where you've built data platforms or worked with Azure services, showcasing your hands-on skills in Python and SQL.
Craft a Compelling Cover Letter: In your cover letter, express your passion for building data platforms and your vision for the Databricks Lakehouse Platform. Mention how your previous experiences align with the responsibilities outlined in the job description, particularly in strategic vision and hands-on implementation.
Showcase Relevant Projects: Include specific examples of past projects that demonstrate your expertise in designing and implementing data solutions on the Databricks platform. Highlight any challenges you faced and how you overcame them, especially in greenfield environments.
Highlight Certifications: If you have any relevant certifications, such as Databricks Certified Data Engineer or Microsoft Certified: Azure Data Engineer Associate, make sure to mention these prominently in your application. They can set you apart from other candidates.
How to prepare for a job interview at Osmii
✨Showcase Your Databricks Expertise
Be prepared to discuss your hands-on experience with the Databricks Lakehouse Platform. Highlight specific projects where you've designed and implemented solutions, focusing on tools like Delta Lake and Spark.
✨Demonstrate Strategic Thinking
Since this role involves shaping the data strategy, be ready to articulate your vision for a greenfield data platform. Discuss how you would approach architectural design and technology selection to meet business needs.
✨Prepare for Technical Challenges
Expect technical questions that assess your problem-solving skills in data ingestion, ETL/ELT processes, and performance optimisation. Brush up on your Python and SQL skills, as practical examples may be requested.
✨Emphasise Team Leadership and Mentorship
This role requires guiding a team of Data Engineers and Analysts. Share your experiences in mentoring others, fostering best practices, and ensuring knowledge transfer within a team environment.
