Data Engineer

Prudential's purpose is to be partners for every life and protectors for every future. Our purpose encourages everything we do by creating a culture in which diversity is celebrated and inclusion assured, for our people, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and we support our people's career ambitions. We pledge to make Prudential a place where you can Connect, Grow, and Succeed.

Data Engineer is responsible to Extract, Load and Transform (ELT) as well as manage the Change Data Capture (CDC) using Qlik Replicate to ingest data into Data Lake, Data Models, Data Marts and 7-Sisters Master Data Management platform that help establish data-driven decision-making across the organization.
This is a new Data Team (Tribe) under IT Department that will drive the Data Strategy for the organization. They will work with respective Data Owners to develop the data models, data marts and data analytics for the whole PruBSN organization, following the regional Data COE approach to maintain good data governance and develop the 7-Sisters Master Data Management. The Data Strategy for PruBSN will be executed via the Hub & Spoke approach, the Hub being this core Data Team under IT, collaborating and working with the respective department Data Stewards and PruBSN Data Heroes (Power BI superusers), i.e. the Spokes.

Key Accountabilities

1. Serve as the technology evangelist to develop integrated solutions that demonstrate business value

2. Work with business stakeholders to understand business priorities and optimize IT strategies to support them. Scope to include full lifecycle, from approach strategy, architecture design, solution development, through installation and deployment

3. Design, develop and implement effective extract, transform and loading (ETL/ELT) based on data needs along with database solutions and models to be able to store and serve the data accordingly

4. Perform assessments/gap analysis of current technology landscape and identify high impact areas of improvement

5. Collaborate with relevant teams to deliver transformation roadmaps, optimization and standardization strategies

6. Review data mapping, design and technical documentation

7. Lead and enable data team members to gain new skillset and apply the skillset for deliverables

8. Able to understand, translate business requirements and work on various source systems (IL, DCMS, RCS, BPM, UME, SunGL etc.) and databases (PostgreSQL, DB2, Oracle, Azure Synapse Analytics, MongoDB, etc.)

Performance Measures

Deliver a Unified Data Platform

Implementing Customer and Contract Master, Product and Pricing Master

Enable Customer Insight Platform

Publish and Subscribe Data for Artificial Intelligence / Business Intelligence use cases

Lead and guide team members to deliver data related projects on time and on budget

Ingest, Curate, and orchestrate data for data consumer

Review data mapping, design and technical documentation

Qualification and Experience

Data Engineer should be familiar with data science, business intelligence, and data analytics. He/she should have prior knowledge of data integration, data warehousing, modeling, business intelligence, and presentation concepts.

Required Skills for Data Engineer:

1. Must-Have Experience With Azure Data Tools:

A Data Engineer must possess experience working with Azure Data tools e.g. Azure Data Factory, Azure Data Lake, Azure Synapse and BI systems like Power BI, Tableau, SAP, and so on. Considering the power bi role, they must have experience in creating data-rich dashboards, writing DAX expressions, and implementing row-level security in Power BI. Also, they must be able to develop custom BI products that require knowledge of scripting languages and programming languages like R and Python.

One of the key attributes of a Data Engineer is to Extract, Load and Transform (ELT) as well as manage the Change Data Capture (CDC) using Qlik Replicate to ingest data into Data Lake, Data Models, Data Marts and 7-Sisters Master Data Management platform that help establish data-driven decision-making across the organization.

2. Required Experience In Data-Specific Roles:

To become a Data Engineer, you should require a minimum experience of 2 or 3 years working with ETL tools, or any data-specific roles. They are expected to have sound knowledge of database management, SQL querying, data modeling, data warehousing and OLAP (Online Analytical Processing).

3. Knowledge In Microsoft BI Stack:

There are many products and services from Microsoft that are widely used by large-scale enterprises for data warehousing, data management, analytics, reporting, and business intelligence. Knowledge and experience with Microsoft Business Intelligence stacks such as Power BI, Power Apps, Power Pivot, SSRS, SSIS, and SSAS is an added advantage.

4. Software Development Skills:

He/she doesn't have to be an expert in software development, but in order to meet diversified needs, they must know how to develop a custom data structure to support BI solution and Application Programming Interface (API). It is essential to have an understanding of technical aspects as well as software development architecture to transform requirements into technical presence.

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 individual physical or mental health requirements.