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: Competitive pay, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with strong career advancement potential.
- Why this job: Be at the forefront of data innovation and make a real impact in the tech world.
- 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, data analytics, Lakehouse architecture, and technology.
Key Roles & Responsibilities
- Discovery and Assessment – Understand what data exists and how it behaves.
- Migration Strategy & Planning – Define how migration will happen.
- Data Mapping & Transformation Design – Translate source data into target structures.
- Data Cleansing & Enrichment – Sanitize data before moving it.
- Migration Architecture & Pipeline Design – Design the technical movement of data.
- Data Migration Development & Testing – Build and validate pipelines.
- Data Reconciliation & Validation – Ensure migrated data is correct.
Develop and execute the enterprise data architecture strategy aligned with the organization’s goals. Collaborate with business leaders to understand data needs and ensure the architecture supports business objectives. Evaluate and recommend data management tools and technologies that align with the organization’s strategic vision. Implement master data management, reference data management, and metadata management strategies to ensure data consistency, quality, and security.
Data Governance And Compliance
- Develop and implement data governance policies and standards, as well as performance indicators and quality metrics, to manage data effectively and ensure compliance with data-related policies and standards.
- Monitor data quality and performance metrics, addressing issues as they arise to maintain data integrity.
Architectural Design
- Design and implement data models, data flows, and data integration strategies to support business processes.
- Develop and maintain comprehensive data architecture documentation, including data models, data dictionaries, and metadata.
- Establish data governance frameworks and best practices to ensure data quality, consistency, and security.
Lakehouse Architecture
- Design and implement Lakehouse architectures that combine the features of data lakes and data warehouses, optimizing for both structured and unstructured data.
- Utilize Lakehouse platforms and tools to integrate, store, and analyze large volumes of data efficiently.
- Evaluate and recommend Lakehouse solutions and technologies, including Delta Lake, Apache Hudi, MS Fabric, Databricks, or Apache Iceberg, to enhance data processing and analytics.
Business Intelligence (BI) Integration
- Design and implement BI architecture to support reporting, analytics, and decision-making processes.
- Develop and maintain BI data models, dashboards, and reports that provide actionable insights to business stakeholders.
- Evaluate and recommend BI tools and technologies to enhance data visualization and analysis capabilities.
Collaboration And Leadership
- Lead cross-functional teams to drive data-related projects and initiatives.
- Communicate data architecture strategies and solutions to stakeholders at all levels, including executives.
- Mentor and provide guidance to junior data architects and data management staff.
Business Expertise – Must-have
- Advanced SQL and data modeling.
- Cloud data platform expertise.
- ETL/ELT and pipeline design.
- Data governance and 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 – Data Architecture & Modeling
- Conceptual, logical, and physical data modeling.
- Dimensional modeling (star/snowflake schemas).
- Normalization vs. denormalization tradeoffs.
- Data vault modeling (increasingly important in modern architectures).
- 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 (Synapse, Data Factory, Fabric), AWS (Redshift, Glue, Lake Formation), Google Cloud (BigQuery, Dataflow).
Understanding of:
- Data lakes vs. lakehouses.
- Distributed storage (S3, ADLS).
- 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 (Kafka, Event Hubs).
Databases & Storage Technologies
- Relational databases (SQL Server, Oracle, PostgreSQL).
- NoSQL (MongoDB, Cassandra, DynamoDB).
- Data warehouse platforms.
- Data lake / lakehouse architectures (Delta Lake, Iceberg).
Skills
- Query optimization.
- Indexing strategies.
- Partitioning.
- Performance tuning.
Data Processing & Engineering
- SQL mastery.
- Python or Scala (for pipelines).
- Spark.
- Familiarity with distributed computing concepts.
Analytics & BI Ecosystem Understanding
- Data warehousing concepts.
- Semantic layers and data marts.
- BI tools (Power BI, Tableau, Looker).
- Query performance design for analytics workloads.
Data Governance, Security & Compliance
- Data governance frameworks.
- Data lineage and metadata management.
- Data catalog tools (Purview, Collibra, Alation).
- Security: encryption (at rest/in transit), RBAC/ABAC, data masking/tokenization, regulatory awareness (GDPR, HIPAA).
Architecture Patterns & Design Skills
- Data mesh vs data warehouse vs data fabric architectures.
- Microservices & domain-driven design (data implications).
- Scalability and high-availability design.
- Cost optimization patterns in cloud.
DevOps & DataOps
- CI/CD pipelines for data (Azure DevOps, GitHub Actions).
- Infrastructure as Code (Terraform, ARM templates).
- Version control (Git).
- Monitoring & observability (data pipelines + quality).
Data Quality & Observability
- Data validation frameworks.
- Data quality rules and monitoring.
- Observability tools (Monte Carlo, Great Expectations).
- Root cause analysis of data issues.
Metadata, Lineage & Cataloging
- Data lineage tracking (end-to-end).
- Business glossaries.
- Metadata management systems.
- Impact analysis capabilities.
Emerging & Advanced Skills
- AI/ML data pipelines (basic understanding).
- Feature stores.
- Real-time analytics.
- Graph databases and knowledge graphs.
- Data products (product thinking applied to data).
Nature & Area Of Impact
- Business stakeholder interaction, decision making, and strategy definition.
Interactions / Interpersonal Skills
- Analytical Thinking: Strong analytical skills with the ability to design and implement complex data solutions.
- Problem-Solving: Excellent problem-solving skills with a proactive approach to resolving data issues.
- Communication: Effective communication skills, with the ability to present technical concepts to non-technical stakeholders.
- Leadership: Proven leadership abilities with experience in managing cross-functional teams and projects.
- Project Management: Strong organizational skills with experience in managing and delivering data projects on time and within budget.
Enterprise Data Architect (CONTRACT POSITION) in Winnersh employer: Loftware
As an Enterprise Data Architect with us, you'll thrive in a dynamic work culture that prioritises innovation and collaboration. We offer competitive benefits, including professional development opportunities to enhance your skills in cutting-edge data technologies, all while working in a vibrant location that fosters creativity and teamwork. Join us to make a meaningful impact on our data strategy and drive the organisation's success.
StudySmarter Expert Advice🤫
We think this is how you could land Enterprise Data Architect (CONTRACT POSITION) in Winnersh
✨Tap into Online Data Science Communities
Join online communities focused on data science like Kaggle, LinkedIn groups, or Reddit threads. These are goldmines for temporary gigs, as you can network with professionals and potentially hear about opportunities at companies like Loftware before they're even advertised!
✨Show Off Your Skills With Projects
Got some cool data science projects? Showcase them on platforms like GitHub or create a personal portfolio website. This visibility is crucial for landing temporary roles—let recruiters see your actual skills in action, which can set you apart from the crowd.
✨Check Out Specialist Job Boards
For temp roles, hit up job boards dedicated to tech and data science, like Stack Overflow Jobs or DataJobs. These platforms often feature openings that you won’t find on general job sites, including contracts with companies like Loftware.
✨Leverage University Resources
If you're currently at uni or recently graduated, tap into your school's career services. They often have connections with companies looking for temporary data science interns or contract workers, and they might even host job fairs with employers like Loftware.
We think you need these skills to ace Enterprise Data Architect (CONTRACT POSITION) in Winnersh
Some tips for your application 🫡
Highlight Your Data Projects:When applying for a temporary data science role at Loftware, make sure to showcase any relevant projects you've worked on. Whether it's a personal project, an academic undertaking, or contributions to an open-source initiative, detailing these experiences can really set you apart and demonstrate your practical skills.
Emphasise Your Analytical Skills:In your CV and cover letter, focus on the specific analytical skills that are key to data science. Mention any experience with statistical tools, programming languages like Python or R, and data visualisation software. Don't forget to include any certifications that may bolster your expertise!
Show Your Flexibility:Since this is a temporary role, it's important to convey your adaptability and willingness to learn. In your cover letter to Loftware, emphasise how quickly you can get up to speed with new tools or projects. Highlight any previous experiences where you've had to adjust to new environments or challenges.
Craft a Unique Data-Driven Cover Letter:Instead of the usual generic cover letter, spice it up with some data! Maybe you’ve improved a process by 20% in a past role or cleaned a dataset with over a million entries. Use these stats to your advantage to grab Loftware’s attention and show the tangible impact of your work.
How to prepare for a job interview at Loftware
✨Showcase Your Analytical Skills
For a data science gig, it's crucial to demonstrate your analytical abilities. Be ready to discuss previous projects and the methodologies you used. Think about how you can quantify your impact—did your analysis improve efficiency or save costs? These are the stories that will stick with interviewers at Loftware.
✨Brush Up on Technical Skills
You might face technical questions on tools relevant to data science, like Python, R, or SQL. Prepare to solve a problem live—perhaps they'll ask you to write a simple query or code snippet. It’s cool to talk about them, but we need to show we can do it in practice, especially in a temporary role where quick results matter.
✨Highlight Your Adaptability
Since this is a temporary position, emphasise your ability to learn quickly and adapt to new tools or workflows. Share examples of how you've thrived in fast-paced environments before, and how you can hit the ground running at Loftware.
✨Prepare a Portfolio of Your Work
Bring your portfolio to the table—showcase projects where you've leveraged data science techniques to solve problems. Whether it’s a GitHub repository or a set of case studies, having tangible examples of your work will help you stand out and show what you bring to the team at Loftware.
