At a Glance
- Tasks: Design and implement data architecture to support business goals and ensure data integrity.
- Company: Join a forward-thinking organisation focused on innovative data solutions.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on mentorship and career advancement.
- Why this job: Shape the future of data architecture and make a significant impact in a dynamic environment.
- Qualifications: Experience in data architecture, SQL, and cloud platforms is essential.
The predicted salary is between 70000 - 90000 £ per year.
The Enterprise Data Architect is responsible for designing, implementing, and maintaining the overall data architecture of the organization. This role involves creating a comprehensive data strategy to support the business's strategic goals, ensuring data consistency, integrity, and availability across various systems. The ideal candidate will have extensive experience in data architecture, data modeling, and data management, with a strong understanding of business intelligence (BI), data analytics, Lakehouse architecture, and technology.
Key Roles & Responsibilities:
- Data Migration Design and Technical Oversight: Understand what data exists and how it behaves; Define how migration will happen; Translate source data into target structures; Fix data before moving it; Design the technical movement of data; Build and validate pipelines; Ensure migrated data is correct; Move into production.
- Data Strategy Development: Develop and execute the enterprise data architecture strategy aligned with the organization’s goals; Collaborate with business leaders to understand data needs; Evaluate and recommend data management tools and technologies; Implement master data management, reference data management, metadata management strategies.
- Data Governance and Compliance: Develop and implement data governance policies and standards; Monitor data quality and performance metrics.
- Architectural Design: Design and implement data models, data flows, and data integration strategies; Develop and maintain comprehensive data architecture documentation; Establish data governance frameworks and best practices.
- Lakehouse Architecture: Design and implement Lakehouse architectures; Utilize Lakehouse platforms and tools; Evaluate and recommend Lakehouse solutions and technologies.
- Business Intelligence (BI) Integration: Design and implement BI architecture; Develop and maintain BI data models, dashboards, and reports; Evaluate and recommend BI tools and technologies.
- Collaboration and Leadership: Lead cross-functional teams; Communicate data architecture strategies and solutions; Mentor and provide guidance to junior data architects and data management staff.
Must-have Skills:
- Advanced SQL + data modeling
- Cloud data platform expertise
- ETL/ELT and pipeline design
- Data governance & security
Strong Differentiators:
- Real-time/event-driven architecture
- DataOps / automation
- Data mesh / modern architecture patterns
- AI/ML data infrastructure and application
- Data observability platforms
Problem Solving:
- Conceptual, logical, and physical data modeling
- Dimensional modeling (star/snowflake schemas)
- Normalization vs. denormalization tradeoffs
- Data vault modeling
- Master Data Management (MDM) concepts
Tools:
- ER/Studio, ERwin, Lucidchart, SQL DB tools
Cloud Data Platforms:
- Deep expertise in at least one major cloud: Azure, AWS, Google Cloud
- Understanding of data lakes vs. lakehouses
- Distributed storage
- Serverless vs provisioned architectures
Data Integration & Pipeline Design:
- ETL / ELT design patterns
- Batch and real-time streaming architectures
- Change Data Capture (CDC)
- API-based integration
- Event-driven architectures
Databases & Storage Technologies:
- Relational databases
- NoSQL
- Data warehouse platforms
- Data lake / lakehouse architectures
Data Processing & Engineering:
- SQL mastery
- Python or Scala
- Spark
- Familiarity with distributed computing concepts
Analytics & BI Ecosystem Understanding:
- Data warehousing concepts
- Semantic layers and data marts
- BI tools
- Query performance design for analytics workloads
Data Governance, Security & Compliance:
- Data governance frameworks
- Data lineage and metadata management
- Security measures
- Regulatory awareness
Architecture Patterns & Design Skills:
- Designing data mesh vs data warehouse vs data fabric architectures
- Microservices & domain-driven design
- Scalability and high-availability design
- Cost optimization patterns in cloud
DevOps & DataOps:
- CI/CD pipelines for data
- Infrastructure as Code
- Version control
- Monitoring & observability
Data Quality & Observability:
- Data validation frameworks
- Data quality rules and monitoring
- Observability tools
- Root cause analysis of data issues
Metadata, Lineage & Cataloging:
- Data lineage tracking
- Business glossaries
- Metadata management systems
- Impact analysis capabilities
Emerging & Advanced Skills:
- AI/ML data pipelines
- Feature stores
- Real-time analytics
- Graph databases and knowledge graphs
- Data products
Nature & Area of Impact: Business stakeholder interaction, decision making, and strategy definition.
Interactions / Interpersonal Skills: Strong analytical skills; Excellent problem-solving skills; Effective communication skills; Proven leadership abilities; Strong organizational skills.
Enterprise Data Architect in Winnersh employer: Loftware External
As an Enterprise Data Architect at our organisation, you will thrive in a dynamic and innovative work culture that prioritises collaboration and professional growth. We offer competitive benefits, including flexible working arrangements and opportunities for continuous learning, all set in a vibrant location that fosters creativity and engagement. Join us to make a meaningful impact on our data strategy while advancing your career in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Enterprise Data Architect in Winnersh
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Loftware External.
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We think you need these skills to ace Enterprise Data Architect in Winnersh
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Loftware External, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Loftware External. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Loftware External
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Loftware External!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
