Job description
Prudential's purpose is to help people get the most out of life. We will deliver our purpose by creating a culture in which diversity is celebrated and inclusion assured, for our colleagues, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and in exchange, we support our people's career ambitions. We pledge to make Prudential a place where you can Connect, Grow and Succeed.
Role Purpose:
The role is within the Global Business Services (GBS) delivery centre in Malaysia, reporting day-to-day to the Data Analysis & Intelligence team Lead, with an ultimate reporting line to the Head of Financial Crime Intelligence Unit Head and Group Chief Security Officer. You will form part of the Financial Crime Monitoring Hub Team (FCMH) team primarily responsible for designing, creating and deploying advanced analytics and machine learning solutions to detect and control fraud in multiple Prudential business units. As a Data Engineer in this team you will be responsible for expanding and optimizing our data and data pipeline architecture, and optimizing data flow and collection for cross-functional and cross-regional teams. The Data Engineer will support software developers, database architects, data analysts and data scientists on data initiatives to ensure optimal data delivery architecture and consistency.
Responsibilities
- Create and maintain optimal data pipeline architecture
- Ability to change ETL logic based on source data
- Assemble large, complex data sets that meet functional / non-functional business requirements
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies
- Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics
- Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
- Keep our data separated and secure across national boundaries through multiple data centers and AWS regions
- Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader
- Work with data and analytics experts to strive for greater functionality in our data systems
Requirements:
Must have experience:
- Fraud, financial crime, customer insight or compliance based projects that utilise detection or predictive models
- Working on projects involving statistical approaches or detection logic
- Agile development methodology
- Strong analytical skills developed through working with big data and data sets
- 5+ years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field.
Must have technical skills:
- Azure Data Bricks, Data Factory and Synapse
- Development environment skills (code control, IDEs), understanding processes such as until testing, design and documentation
- Data mapping, relational data and object orientation
- Experience of working on operational environments (BitBucket, Artifactory Jenkins, Azure DevOps, BitBlocks)
- Scripting languages: Python, Java, C++, Scala, etc
- Google Cloud big data services, such as ElasticSearch
- Understanding of Azure Active Directory and SSO set-up
- Experience of SAS Application and set-up
Good to have experience:
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of SQL and noSQL databases.
- Building and optimizing ‘big data’ data pipelines, architectures and data sets.
- Performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
- Strong analytic skills related to working with unstructured datasets.
- Build processes supporting data transformation, data structures, metadata, dependency and workload management.
- A successful history of manipulating, processing and extracting value from large disconnected datasets.
- Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
- Strong project management and organizational skills.
- Supporting and working with cross-functional teams in a dynamic environment.
- Experience/familiarity with a broad range of tools and environments, such as
- Big data tools: Hadoop, Spark, Kafka, etc.
- Relational SQL and NoSQL databases, including Postgres and Cassandra.
- Data pipeline and workflow management tools
- Stream-processing: Storm, Spark-Streaming, etc.
Prudential is an equal opportunity employer. We provide equality of opportunity of benefits for all who apply and who perform work for our organisation irrespective of sex, race, age, ethnic origin, educational, social and cultural background, marital status, pregnancy and maternity, religion or belief, disability or part-time / fixed-term work, or any other status protected by applicable law. We encourage the same standards from our recruitment and third-party suppliers taking into account the context of grade, job and location. We also allow for reasonable adjustments to support people with special requirements.
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