VP - Data Engineering - Asset Management

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.

• To work collaboratively with colleagues and take personal accountability to maintain and enhance controls you are responsible for to support improvement of the overall control environment, customers outcomes and a reduction in M&G plc's operational risk. • Hands-on engineering – Personally design, build and review data pipelines, transformations, models, APIs and data services on Azure and Snowflake, retaining deep hands-on involvement in the most complex or highest-risk engineering work. • Hands-on engineering – Own the technical design and build of ingestion, curation and serving layers using Snowflake for data storage, curation, provisioning, secure sharing and consumption. Working knowledge of Azure SQL, Azure Data Lake Storage, Azure Data Factory and Azure Databricks is desirable to move workloads to Snowflake as appropriate. • Hands-on engineering – Optimise platform performance and cost, including Snowflake warehouse sizing, query tuning, clustering, caching, Databricks cluster configuration and pipeline efficiency, with clear, measurable outcomes. • Hands-on engineering – Lead root cause analysis of complex production failures and data-quality incidents, implementing sustainable corrective actions rather than tactical fixes. • Hands-on engineering – Set and enforce engineering standards for coding, data modelling, testing, version control, code review, documentation, security and release management. • Leadership – Lead, coach and develop the data engineering team, setting clear expectations, objectives and development plans, and actively raising technical depth and engineering maturity. • Leadership – Raise the engineering bar through structured code review, pairing, technical forums, reusable patterns, internal tooling and a strong culture of craftsmanship and peer challenge. • Leadership – Own skills planning and succession for the engineering team, including recruitment, onboarding, capability uplift and reduction of key-person dependency. • Leadership – Plan and manage engineering delivery across parallel workstreams, providing clear visibility of progress, capacity, risks, dependencies and technical debt. • Leadership – Drive DataOps and automation, including CI/CD, infrastructure-as-code, automated data-quality and regression testing, environment management and controlled release, to improve delivery speed and reduce toil. • Platform – Contribute meaningfully to the advancement of the Asset Management data platform, shaping architecture, technology choices, platform roadmap and engineering strategy in partnership with the UK Head of Data Engineering and Architecture. • Platform – Champion data product and data mesh engineering patterns, including domain-aligned pipelines, data contracts, schema evolution, reusable transformation frameworks and self-serve data infrastructure. • Platform – Embed data quality, metadata, lineage and control requirements into engineering solutions by design, working closely with Data Governance, Data Owners and Business Data Analysts. • Platform – Ensure security, resilience and compliance, covering identity and access management, secrets management, encryption, data privacy, observability and operational resilience obligations. • Platform – Evaluate and introduce new engineering capability, including Snowflake and Azure feature adoption, streaming and event-driven patterns, and AI-assisted engineering, with clear assessment of value, risk and total cost. • Collaboration – Partner with Business Data Analysts, Data Product Owners, Architecture, Quality Engineering and Production Support to deliver complete, supportable data products rather than isolated components. • Collaboration – Represent India data engineering in global forums, providing clear technical communication, delivery confidence and trade-off analysis to senior UK and India stakeholders. • Collaboration – Manage partner and vendor engineering contributions, including quality assurance, knowledge retention and in-sourcing where appropriate. • Collaboration – Use approved AI-assisted engineering tools responsibly for development, debugging, test generation and documentation, treating generated outputs as untrusted until independently reviewed, tested and security-checked.

We have a diverse workforce and an inclusive culture at M&G Global Services, regardless of gender, ethnicity, age, sexual orientation, nationality, disability or long term condition, we are looking to attract, promote and retain exceptional people. We also welcome those who take part in military service and those returning from career breaks.