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    Data Architect - London, United Kingdom - Aventum Group

    Aventum Group
    Aventum Group London, United Kingdom

    6 days ago

    Default job background
    Description

    Title:
    Data Architect


    Reporting to:
    Head of Data

    Location:
    Hybrid - London office 2/3 days a week

    The role of a Data Architect is a visionary who translates business requirements into technology requirements and defines data standards and principles.

    The data architect is responsible for visualizing and designing the organisation's enterprise data management framework for how IT infrastructure supports our data strategy.

    This role aims to show the company's infrastructure how data is acquired, transported, stored, queried, and secured. The data architecture built will be the foundation of any data strategy.
    Responsible for visualising and designing the organisation's enterprise data management framework of today.

    This framework will describe the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data.

    Responsible for understanding the overall TO-BE data architecture and ensuring that it meets the data needs of businesses across the business.

    Typically involved in defining how data will be collected, stored, and consumed e.g. MI/ML Projects

    Planning the implementation of solutions, choosing the right technologies and evaluating the evolution of the architecture as the organisation' needs change.

    Envision, build, deploy and operationalise an end-to-end machine learning (ML) and AI pipeline based on needs.
    Responsible for visualising the "blueprint" of the complete framework that data engineers then build
    Translating business requirements into technical specifications, including data streams, integrations, transformations, databases, and data warehouses e.g. develop and optimise database solutions to store and retrieve company information.
    Defining the data architecture framework, standards, and principles, including modelling, metadata, security, reference data such as country codes and security reference data that covers all issued securities across diverse asset classes, and master data such as details about policyholders (like name, address, contact details, and demographic data), policy details (like policy number, coverage, terms and conditions), product data (like product type, premiums, and benefits), and claims data (like claim number, claim amount, and claim status) and employees
    Defining reference architecture, which is a pattern others can follow to create and improve data systems
    Defining data flows, i.e., which parts of the organisation generate data, which require data to function, how data flows are managed, and how data changes in transition
    Analyse structural requirements for new software and applications
    Migrate data from legacy systems to new solutions
    Plan for third-party data integration to new solutions
    Improve system performance by conducting tests, troubleshooting and integrating new elements

    Bachelor's degree in Computer Science, Computer Engineering, or related field; or equivalent education/experience mix covering data management, programming, application design, big data development, systems analysis, and technology architectures.

    Proficient in core data architect skills, emphasizing data modeling, SQL development, and database administration.

    In-depth understanding of the System Development Life Cycle (SDLC), project management, architecture approaches, requirements analysis, design, and testing techniques.

    Familiarity with established and emerging data management and reporting technologies, including columnar and NoSQL databases, predictive analytics, data visualization, and unstructured data.

    Ability to create diagrams of key data entities and relationships and generate a component list for system construction.
    Strong interpersonal skills, articulation, persuasion, political savvy, and effective communication of the big data vision.
    Proven experience in analyzing structural requirements for new software and applications.
    Track record in analyzing and mapping current and future data models, overseeing transition activities.
    Diligent cataloguing and control of data objects for re-use and design purposes.
    Proficiency in designing models for data processing aligned with the business model.
    Strong understanding of cloud computing, databases, and relevant applications, covering all Data Modeling phases, including conceptualization and optimization.

    Cloud Computing platforms (ADLS Gen2), Microsoft Stack (Synapse, DataBricks, Fabric, Profisee), Snowflake Data Integration, Azure Service Bus, Apache Airflow, Apache Iceberg, Apache Spark, Apache Hudi, Apache Kafka, Power BI, BigQuery, DeltaLake, Azure DevOps, Azure Monitor, Azure Data Factory, SQL Server, Azure DataLake Storage, Azure App Service, Azure ML is a plus.


    Languages :
    Python, SQL, T-SQL, SSIS, and high-level programming knowledge on Spark is a plus.

    DB:
    Azure SQL Database, Cosmos DB, NoSQL, MongoDB, and HBase are a plus
    Agile and DevOps must have

    Concepts:
    ELT/ETL, DWH, APIs (RESTful), Spark APIs, FTP protocols, SSL, SFTP, PKI (Public Key Infrastructure) and Integration testing
    #
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