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About M&G; India We are M&G; India, the strategic innovation and digital hub for M&G.; Established in 2003, we have offices in Mumbai and Pune. Our teams work closely with colleagues across the Group worldwide to drive transformation, build digital capability, and support sustainable growth. By leveraging technology, AI, automation, and process excellence, we bring new ways of thinking to improve outcomes for both customers and colleagues. Grounded in a vibrant culture with strong foundations, we are central to how M&G; is transforming as a business. About M&G; Our purpose is to give everyone real confidence to put their money to work. With a heritage dating back more than 175 years, we have a long history of innovation in savings and investments, combining asset management and insurance expertise to offer a wide range of solutions. Our two distinct operating segments, Asset Management and Life, work together to provide access to balanced, long-term investment and savings solutions. Through telling it like it is, owning it now, and moving it forward together with care and integrity; we are creating an exceptional place to work for exceptional talent. Overall Job Purpose Individual would be part of the Methodology & Assumption (M&A;) Actuarial Policyholder Data Team within the Actuarial Modelling & Valuation Centre of Excellence in Finance. A new solution for the validation and transformation of policyholder data used in actuarial modelling and assumption setting is being built. This role will operate, maintain and make BAU updates to the current data pipelines which will validate, transform and enrich data for use in actuarial modelling and assumption setting. It will use its strong data analysis skills to investigate and resolve data issues found pre-emptively or those raised by stakeholders. The role contributes to the continuous improvement of data processes, ensuring accuracy, consistency, and clarity in outputs while working within defined guidelines. Communication of findings is clear, structured, and tailored to support stakeholders in progressing with confidence. Individual is accountable and responsible for the Model Points Data Production and Experience Analysis related tasks as assigned by the Team Manager. The person would act as necessary support to the team by carrying the tasks for the process with an acceptable standard of quality and within agreed timelines. Primary Key Responsibilities - Design, build, and maintain scalable data pipelines that validate and transform data to meet actuarial modelling requirements. - Implement checks and controls to ensure actuarial input data is complete, accurate, reconciled and available when required. - Work with data suppliers to ensure data loads are complete and controlled. - Monitor and optimise data performance, resolving issues and improving efficiency where needed. - Maintain runbooks or support documentation - Maintain process documentation, data dictionaries, and operational procedures - Ensure all enhancements are appropriately documented and meet business requirements before implementation. Additional Responsibilities : - Collaborate with stakeholders to understand data needs and translate them into effective technical solutions. - Works independently on well-defined deliverables and escalates material risks and issues appropriately - Work with suppliers and actuarial users to resolve data exceptions - Contribute to data governance, including documentation, standards, and controls. - Deliver high-quality outputs with strong attention to detail, ensuring accuracy, consistency, and compliance with agreed standards - Work independently within established frameworks, managing own and team s workload management. Key Stakeholder Management Internal M&G; India Team manager UK Team manager Other senior stakeholders Risk Team External Diligenta PwC WTW Knowledge, Skills, Experience & Educational Qualification Knowledge & Skills (Must Have) : - Strong understanding of data structures, databases, and data modelling concepts. - Experience working on data pipelines with data transformation, validation and storage processes. - Ability to manage and process large datasets with attention to accuracy and quality. - Familiarity with data governance and best practices in data management. - Strong problem-solving skills and the ability to work collaboratively across teams. - Experience working in Financial Services or a similar heavily audited environment with an understanding of operating controls and producing audit evidence - Clear communication with both technical and non-technical stakeholders. - Proactive approach to identifying and resolving issues. - Commitment to continuous improvement and delivering high-quality outcomes. - SQL - Data management and modelling - Experience maintaining documentation, controls, and governance evidence to support auditability and operational resilience - Experience using Excel, Power BI .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.