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Data Architect And Engineering Manager

  • Location: City of London
  • £ Salary: Negotiable per year
  • Job Type:Permanent

Posted over 1 year ago

  • Sector: Financial Services
  • Contact: James Taylor
  • Start Date: ASAP
  • Expiry Date: 28 October 2022
  • Job Ref: JN -092022-474230

Hydrogen Group are currently looking to recruit a Data Architect & Engineering Manager for a financial services organisation based in central London. The Data Architect & Engineering Manager will work a hybrid work pattern of 2 days in the office and 3 from home.

The Data Architect & Engineering Manager is responsible for the successful delivery and development of the company's Data Platforms (including the Enterprise Data Warehouse and Azure Data Lake). The Data Architect & Engineering Manager will manage a team of data engineers and data scientists and work across the scheme with colleagues and partners to develop plans for the short and long term data architecture and engineering to deliver on the company's customer and consumer strategies.

Responsibilities

· Technical oversight driving the short and long term data architecture.

· Establish a cohesive agile matrix structure in which engineering teams work 'hand in glove' with product managers and stakeholders scheme wide.

· Data Solution architecture and design.

· Provide a forward view of industry standards and emerging trends.

· Define technical requirements, drive and support technology selections

· Manage the engineering team during different phases of delivery including estimates, technical design and code reviews

· Drive and support the development of a data architecture and engineering strategy and roadmap which enables the company to develop a data driven organisation by establishing centralised and de-coupled architectures.

Required Technical Skills

· Excellent SQL Server skills

· Excellent understanding of cloud computing concepts, design patterns and technologies

Experience solving problems in areas such as:

o NLP

o Forecasting

o Bayesian

o Reinforced learning

o Deep Learning

o Multi-variate testing

o Strong SQL and Python (NumPy, SciPy, Pandas, Scikit-learn, matplotlib, shap….)

o Experience with major cloud computing providers: AWS, GCP, Azure

· Excellent Azure data platform skills

· Understanding and experience of data structures, including data dictionary and building data models

· Strong Data Modelling (Conceptual, Relational and Dimensional)

· Advanced knowledge of Data Warehouse Modelling and Kimball methodology with an eye on emerging trends such as advanced analytics developing de-coupled architectures

· Analysis of data storage structures including; staging area design, operational data stores, data warehouse design, and data mart design

· ETL, SQL Stored Procedures and Functions

· Design of complex SSIS or DTSX packages using Package and Project deployment configurations

· Strong experience of Self-service BI, data visualization and exploration tools (e.g. Power BI)

· End-to-end data warehousing architecture

· Industry Expertise: Data Delivery & Data Warehouse, Master Data Management, Metadata Management, Data Quality, Data Governance and Stewardship, Analytics

· Knowledge of Big Data, noSQL and Cloud technologies as well as Polyglot Persistence and Lambda Architecture

· Knowledge of Data Science theory and practice (including technical platform requirements)

Further Experience required

· Experience of leading a technical development team

· Experience of database design and data warehouse implementation experience using Microsoft SQL Server, SSIS, SSRS and other tools

· Experience of developing and implementing data solutions in a high-volume data management environment

· Performance tuning, query tuning, troubleshooting and driving problem resolution.

· Experience of defining and implementing Data Architectures

· Solution design and architecture

· Significant experience and clear demonstration of T-SQL programming

· Excellent understanding of SSIS framework, administration, maintenance and configuration

· Experience of using Microsoft Azure's data platform components (Data Lake, Data Warehouse, Data Factory, Blob Storage) desirable

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