Data Engineer

at HSBC BANK PLC HBEU GB ( London, UK)

GBM Data Assets:

Underpinning all of this business insight are data assets; data assets are curated, enriched and protected sets of data – created from global, raw, structured and unstructured sources. Creating reusable data assets, through our Data Factory, allows us to expose, new, actionable insight, back to the business. Our Data Science & Engineering teams are partnering with IT to deliver this ecosystem now. Our GBM Big Data Lake is the single largest aggregation of data ever at HSBC. We have over 300 sources which equates to over 20PTB of data, with a business use case portfolio greater than 110 items. We are also utilising the latest machine learning tools and technologies to solve these hypotheses of today AND tomorrow.

Required Skills:

GBM are looking for a Big Data Engineer that will work on the collecting, storing, processing, and analysing of huge sets of data. The primary focus will be on choosing optimal solutions to use for these purposes, then maintaining, implementing, and monitoring them. You will also be responsible for integrating them with the architecture used across the company and to help build out some core services that power our Machine Learning and advanced analytics systems

Responsibilities:

Developing tools and systems to help analyse and manage large (sometimes unstructured) datasets • Selecting and integrating any Big Data frameworks required to provide requested capabilities • Implementing ELT (Extract, Load & Transform) processes (within HDP & Cloud environment) • Monitoring performance and advising any necessary infrastructure changes • Construct robust data pipelines ready for Machine Learning & NLP engines • Construct and Engineer new Features and Variables which are pertinent for Predictive Models • Help develop prototype machine learning models into robust production systems.

Principal Accountabilities:

In addition to advocating the benefits of using data for business insights and transforming attitudes towards using data. Therefore, driving better business decisions within GBM, objectives 1-4 are the key responsibility of this new role which consists of implementing four types of analytical principles for prioritized business challenges;

  1. Descriptive
  2. Diagnostic
  3. Predictive
  4. Prescriptive

Roll Summary:

This advertised role is key in the overall Data Science team with a reporting line to the Head of Business Insight and Analytics. It is therefore essential that the incumbent has a broad view of the latest and most effective tools and techniques to execute scalable actionable insights as well as a good understanding of the Hadoop ecosystem architecture.

The main focus of the role is on the provision of advice and guidance on implementation of the Data Science Strategy and on implementation of prioritized business challenges using one or more analytics principles. Fundamental to the success of this role will be the ability to deliver business benefit through build and change analytic solutions / models / dashboards / reports within the Data Science Operating Model.

Customers / Stakeholders:

Senior business heads (Markets, Banking, Operations, IT)

Leadership & Teamwork:

  • Motivate and collaborate with the data science team.
  • Establishing and engaging a trusted network of data practitioners/champions across GBM

Operational Effectiveness & Control:

  • Identification and escalation of weaknesses in the accuracy of regulatory reporting
  • Implementation of a risk based approach in the provision of advice and guidance
  • Adherence to project & program life-cycles with appropriate quality gates that enforce best data practice

Major Challenges:

  • Understanding complex regulatory reform across a range of GBM businesses including front-office
  • Articulating complex data issues in a simple way to senior stakeholders
  • Sensitivity to regulators and HSBC’s stance to the deferred prosecution agreement
  • Timely implementation of the Data Science Strategy Framework in tight fiscal circumstances

Management of Risk:

  • Management of data security
  • Identification of weaknesses in regulatory reporting
  • Appropriate controls and discipline for aspects of data change within Projects and Programs
  • Management of controls around data access and processing of high risk information

Observation of Internal Controls:

  • Adherence to ITSR and data security
  • Data Sharing and production controls

Knowledge & Experience / Qualifications:

  • Experience in implementing Data Science techniques including the application of insights
  • Exposure to the financial & banking sector across combinations of client onboarding, risk functions, operations, Client MI, - Regulatory data change, data quality, front office operations
  • Exposure to Global Banking & Markets
  • Exposure to formal Data Science process lifecycles and methodologies (e.g. CRISP-DM, KDD, TDSP)