Data Architect

Mastek
4 days ago
Create job alert

Job Title: Data Architect

Location: London - 3 days travel to office

SC Cleared: Required

Job Type: Full-Time

Experience: 10+ years


Job Summary:

We are seeking a highly experienced and visionary Data Architect to lead the design and implementation of the data architecture for our cutting-edge Azure Databricks platform focused on economic data. This platform is crucial for our Monetary Analysis, Forecasting, and Modelling efforts. The Data Architect will be responsible for defining the overall data strategy, data models, data governance framework, and data integration patterns. This role requires a deep understanding of data warehousing principles, big data technologies, cloud computing (specifically Azure), and a strong grasp of data analysis concepts within the economic domain.


Key Experience:

  • Extensive Data Architecture Knowledge: They possess a deep understanding of data architecture principles, including data modeling, data warehousing, data integration, and data governance.
  • Databricks Expertise: They have hands-on experience with the Databricks platform, including its various components such as Spark, Delta Lake, MLflow, and Databricks SQL. They are proficient in using Databricks for various data engineering and data science tasks.
  • Cloud Platform Proficiency: They are familiar with cloud platforms like AWS, Azure, or GCP, as Databricks operates within these environments. They understand cloud-native data architectures and best practices.
  • Leadership and Communication Skills: They can lead technical teams, mentor junior architects, and effectively communicate complex technical concepts to both technical and non-technical stakeholders.


Responsibilities:


Data Strategy & Vision:

  • Develop and articulate the overall data strategy for the economic data platform, aligning it with business objectives and strategic themes.
  • Define the target data architecture and roadmap, considering scalability, performance, security, and cost-effectiveness.
  • Stay abreast of industry trends and emerging technologies in data management and analytics.


Data Modelling & Design:

  • Design and implement logical and physical data models that support the analytical and modelling requirements of the platform.
  • Define data dictionaries, data lineage, and metadata management processes.
  • Ensure data consistency, integrity, and quality across the platform.


Data Integration & Pipelines:

  • Define data integration patterns and establish robust data pipelines for ingesting, transforming, and loading data from diverse sources (e.g., APIs, databases, financial data providers).
  • Work closely with data engineers to implement and optimise data pipelines within the Azure Databricks environment.
  • Ensure data is readily available for modelling runtimes (Python, R, MATLAB).


Data Governance & Quality:

  • Establish and enforce data governance policies, standards, and procedures.
  • Define data quality metrics and implement data quality monitoring processes.
  • Ensure compliance with relevant data privacy regulations and security standards.


Technology Evaluation & Selection:

  • Evaluate and recommend appropriate data management technologies and tools for the platform.
  • Conduct proof-of-concepts and technical evaluations to validate technology choices.
  • Work with vendors and partners to ensure successful implementation of chosen technologies.


Collaboration & Communication:

  • Collaborate closely with data scientists, economists, business stakeholders, and other technical teams to understand their data needs and translate them into technical solutions.
  • Communicate data architecture concepts and designs effectively to both technical and non-technical audiences.
  • Mentor and guide other team members on data architecture best practices.


Data Security:

  • Work with security teams to ensure that data security policies and procedures are implemented and followed.
  • Define data access controls and ensure that sensitive data is protected.


Qualifications and Skills:

  • 10+ yearsof experience in data management, with at least5+ yearsin a Data Architect role.
  • Deep understanding of data warehousing principles, data modelling techniques (e.g., dimensional modelling, data vault), and data integration patterns.
  • Extensive experience with big data technologies and cloud computing, specifically Azure (minimum3+ yearshands-on experience with Azure data services).
  • Strong experience with Azure Databricks, Delta Lake, and other relevant Azure services.
  • Active Azure Certifications:At least one of the following is required:
  • Microsoft Certified: Azure Data Engineer Associate
  • Microsoft Certified: Azure Data Scientist Associate
  • Active Databricks Certifications:At least one of the following is required:
  • Data Engineer Associate or Professional
  • ML Engineer Associate or Professional
  • Experience in designing and implementing data governance frameworks and data quality processes.
  • Experience working with large datasets and complex data landscapes.
  • Familiarity with economic data and financial markets is highly desirable.
  • Excellent communication, interpersonal, and presentation skills.
  • Strong analytical and problem-solving skills.
  • Experience with data visualisation tools (e.g., Tableau, Power BI).
  • Experience with metadata management tools e.g. Purview.
  • Knowledge of data science and machine learning concepts.
  • Experience with API design and development.

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect & Team Lead - Data Security

Data Architect

Data Architect - Outside IR35 (Basé à London)

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Navigating AI Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

The field of Artificial Intelligence (AI) is growing at an astonishing pace, offering a wealth of opportunities for talented professionals. From machine learning engineers and data scientists to natural language processing (NLP) specialists and computer vision experts, the demand for skilled AI practitioners continues to surge in the UK and globally. AI career fairs present a unique opportunity to connect face-to-face with potential employers, discover cutting-edge innovations, and learn more about the rapidly evolving landscape of data-driven technologies. Yet, attending these events can feel overwhelming: dozens of companies, queues of applicants, and only minutes to make a great first impression. In this detailed guide, we’ll walk you through strategies to prepare for AI career fairs, provide you with key questions to ask, highlight examples of relevant UK events, and reveal the critical follow-up tactics that will help you stand out from the crowd. By the end, you’ll be armed with the knowledge and confidence to land your dream role in the ever-growing world of Artificial Intelligence.

Common Pitfalls AI Job Seekers Face and How to Avoid Them

The global demand for Artificial Intelligence (AI) specialists continues to rise, with organisations across industries keen to implement machine learning, deep learning, and data-driven insights into their operations. Yet, as the market for AI professionals flourishes, so does the level of competition among candidates. Talented individuals who may otherwise be qualified often stumble on common pitfalls that can hinder their success in securing an AI-related role. These pitfalls can lie in their CV, interview approach, job search strategy, or even their understanding of what AI employers are looking for. This article aims to help job seekers in the UK’s AI sector—whether you’re fresh out of university, transitioning into AI from another field, or looking for a senior-level position—avoid the most common mistakes. We’ll discuss how to stand out in a crowded AI job market by improving your CV, acing interviews, and conducting an effective job search. Read on to discover the typical missteps AI professionals make when seeking employment and learn the strategies to avoid them.

Career Paths in Artificial Intelligence: From Research to Management – How to Progress from Technical Roles to Leadership and Beyond

Artificial Intelligence (AI) stands at the forefront of technological innovation, shaping everything from healthcare diagnostics to autonomous vehicles and natural language processing. With the UK widely recognised as a growing hub for AI research and development, there has never been a better time to explore a career in artificial intelligence—or to advance your current trajectory within the field. A key question that often arises is: How can professionals move from hands-on technical roles in AI to leadership and management positions? This comprehensive guide will walk you through the evolving career landscape in AI, from entry-level posts to executive roles. We will examine in-demand skills, recommended pathways for professional development, and strategies to help you seamlessly ascend from technical responsibilities to strategic leadership. Whether you’re a recent graduate, a self-taught data whizz, or an experienced machine learning engineer aspiring to lead teams, this article will provide you with practical insights tailored to the UK’s vibrant AI sector.